{"id":165231,"date":"2025-10-29T10:24:25","date_gmt":"2025-10-29T10:24:25","guid":{"rendered":"https:\/\/m3globalresearch.blog\/?p=165231"},"modified":"2025-10-29T10:24:25","modified_gmt":"2025-10-29T10:24:25","slug":"federated-learning-cancer-research","status":"publish","type":"post","link":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/","title":{"rendered":"Federated Learning in Cancer Research: Enabling Multi-Centre AI While Preserving Patient Privacy","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"165231\" class=\"elementor elementor-165231\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f778131 e-con-full e-flex e-con e-parent\" data-id=\"f778131\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-f1546a2 e-con-full e-flex e-con e-child\" data-id=\"f1546a2\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-eb47e7d elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"eb47e7d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: left;\"><strong>Federated learning in cancer research is redefining how institutions collaborate without compromising patient privacy. From tumour imaging to genomics and clinical outcomes, multi-centre AI collaboration is unlocking new possibilities in precision oncology. Yet, challenges remain before federated learning becomes a standard in cancer research. This article discusses the role of federated learning tumour research, highlighting current applications, barriers to cross-institutional AI collaboration, and future potential for privacy-preserving machine learning approaches in oncology. <\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-08f5c02 elementor-widget elementor-widget-text-editor\" data-id=\"08f5c02\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Artificial intelligence has become a cornerstone of modern oncology, accelerating diagnosis, prognosis, and therapeutic decision-making. But as datasets grow larger and more complex, spanning histopathology slides, radiomics, and multi-omics profiles, researchers face a familiar obstacle: data privacy.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e6cd3a0 elementor-widget elementor-widget-text-editor\" data-id=\"e6cd3a0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Traditional machine-learning models rely on centralised datasets, requiring institutions to share raw patient data. For cancer research, this is increasingly unfeasible due to regulations such as the <a href=\"https:\/\/gdpr-info.eu\/\" target=\"_blank\" rel=\"noopener\">General Data Protection Regulation (GDPR)<\/a> and <a href=\"https:\/\/www.hhs.gov\/hipaa\/index.html\" target=\"_blank\" rel=\"noopener\">HIPAA<\/a>, as well as the logistical barriers of transferring massive, heterogeneous data.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f78b043 elementor-widget elementor-widget-text-editor\" data-id=\"f78b043\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Federated learning (FL) offers a transformative solution. Rather than pooling data into a single repository, federated learning in cancer research enables each centre to train locally and contribute updates to a global model. This distributed machine learning in oncology preserves patient privacy and confidentiality while capturing the statistical power of multi-centre data.<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2001037025002223\" target=\"_blank\" rel=\"noopener\">*<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-66629ae elementor-widget elementor-widget-text-editor\" data-id=\"66629ae\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>In this article, we explore how federated learning supports cancer and tumour research, its current applications, the challenges of multi-centre AI collaboration, and its future role in privacy-preserving machine learning across oncology. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-94d9ec0 elementor-widget elementor-widget-text-editor\" data-id=\"94d9ec0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul><li><a href=\"#Federatedlearning\">Understanding Federated Learning in Oncology and Tumour AI<\/a><\/li><li><a href=\"#tumourimaging\">Current Applications of Federated Learning in Cancer Research<\/a><\/li><li><a href=\"#precisiononcology\">Technical and Regulatory Challenges in Multi-Centre AI Collaboration<\/a><\/li><li><a href=\"#genomics\">Future Prospects of Federated Learning in Oncology and Tumour Research<\/a><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-56c8034 elementor-widget elementor-widget-text-editor\" data-id=\"56c8034\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>How do you see federated learning reshaping collaboration between oncology centres in your field? Share your perspective in the <a href=\"#comments2910\">comments section<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-502faf2 elementor-widget elementor-widget-menu-anchor\" data-id=\"502faf2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"Federatedlearning\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ce3d359 elementor-widget elementor-widget-spacer\" data-id=\"ce3d359\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-15be6b1 elementor-widget elementor-widget-image\" data-id=\"15be6b1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.m3globalresearch.com\/blog\/wp-content\/uploads\/2025\/10\/federated-learning-in-cancer-research-scaled.jpeg\" title=\"federated learning in cancer research\" alt=\"Explore tumour imaging, genomics, and federated learning in cancer research to advance precision oncology and personalised cancer treatment.\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-89f68aa elementor-widget elementor-widget-spacer\" data-id=\"89f68aa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e08694f elementor-widget elementor-widget-heading\" data-id=\"e08694f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Understanding Federated Learning in Oncology and Tumour AI <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b54b4db elementor-widget elementor-widget-text-editor\" data-id=\"b54b4db\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Federated learning operates through a decentralised training structure. A central server hosts the global model, which is shared with participating cancer centres. Each site \u2013 whether a hospital, cancer research centre or biobank \u2013 trains the model on its local data, such as tumour images or genomic profiles. Only model updates are transmitted for aggregation, never raw patient data. This privacy-preserving machine learning maintains confidentiality while capturing the statistical power of multi-centre data.<a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10897620\/\" target=\"_blank\" rel=\"noopener\">*<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-17c7248 elementor-widget elementor-widget-text-editor\" data-id=\"17c7248\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>This method offers several advantages, such as:<br \/><br \/><\/p><ul><li><strong>Data security<\/strong>: sensitive patient data never leaves the originating site;<\/li><li><strong>Scalability<\/strong>: multiple institutions can contribute without data transfer restrictions;<\/li><li><strong>Model robustness<\/strong>: training across diverse datasets improves generalisability.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1168736 elementor-widget elementor-widget-text-editor\" data-id=\"1168736\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>For oncology, these features are game-changing. Cancer is highly heterogeneous: biologically, demographically, and clinically. Multi-centre AI collaboration helps models learn from this diversity, improving their predictive power across populations.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6663eac elementor-widget elementor-widget-text-editor\" data-id=\"6663eac\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>For instance, a distributed machine-learning framework for non-small-cell lung cancer (NSCLC) survival prediction combined datasets from several radiotherapy centres.<a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10072228\/\" target=\"_blank\" rel=\"noopener\">*<\/a> Each site trained its local model, and federated aggregation produced a final system that outperformed single-institution models in both accuracy and external validity.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0219050 elementor-widget elementor-widget-text-editor\" data-id=\"0219050\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Ultimately, federated learning offers the dual benefit of privacy preservation and improved scientific rigour \u2013 two long-standing challenges in oncology data science.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-31e6a48 elementor-widget elementor-widget-menu-anchor\" data-id=\"31e6a48\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"tumourimaging\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ec24e97 elementor-widget elementor-widget-spacer\" data-id=\"ec24e97\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ebc5bc6 elementor-widget elementor-widget-image\" data-id=\"ebc5bc6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.m3globalresearch.com\/blog\/wp-content\/uploads\/2025\/10\/federated-learning-scaled.jpeg\" title=\"Federated learning\" alt=\"Explore tumour imaging, genomics, and federated learning in cancer research to advance precision oncology and personalized cancer treatment.\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-57cc46a elementor-widget elementor-widget-spacer\" data-id=\"57cc46a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-88db546 elementor-widget elementor-widget-heading\" data-id=\"88db546\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Current Applications of Federated Learning in Cancer Research <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-458caf3 elementor-widget elementor-widget-text-editor\" data-id=\"458caf3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Federated learning has rapidly transitioned from a theoretical concept to an applied methodology in oncology. Current examples illustrate its growing clinical relevance:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a20536 elementor-widget elementor-widget-text-editor\" data-id=\"7a20536\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li><strong>Radiotherapy outcome modelling<\/strong>: multi-centre federated frameworks have been used to predict overall survival in NSCLC, incorporating dosimetric and imaging data from independent hospitals. The global model maintained performance parity with centrally trained equivalents while meeting data-protection standards.<a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/38631978\/\" target=\"_blank\" rel=\"noopener\">*<\/a><\/li><br>\n \t<li><strong>Histopathology and tumour segmentation<\/strong>: Federated learning has shown strong potential in digital pathology, enabling algorithms to detect tumour boundaries across diverse scanners and staining protocols. This multi-institution approach improves consistency in pathology AI tools.<a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC9340569\/\" target=\"_blank\" rel=\"noopener\">*<\/a><\/li><br>\n \t<li><strong>Breast, prostate, and lung cancer diagnostics<\/strong>: a systematic review published in Frontiers in Oncology found that federated learning outperformed conventional models in over half of the studies reviewed, particularly in image-based classification and prognostic tasks.<a href=\"https:\/\/www.frontiersin.org\/journals\/oncology\/articles\/10.3389\/fonc.2025.1675459\/full\" target=\"_blank\" rel=\"noopener\">*<\/a><\/li><br>\n \t<li><strong>Genomics and multimodal data<\/strong>: Emerging research is applying federated models to integrate clinical and omics data, enabling risk stratification while respecting privacy.<a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC8810682\/\" target=\"_blank\" rel=\"noopener\">*<\/a><\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-072c816 elementor-widget elementor-widget-text-editor\" data-id=\"072c816\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>These examples demonstrate that federated learning in tumour research is no longer purely experimental. It is a scalable framework capable of uniting oncology data silos worldwide that improves external validity and reduces bias by learning from heterogeneous datasets.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-30064ff elementor-widget elementor-widget-text-editor\" data-id=\"30064ff\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Moreover, the alignment between privacy-preserving architecture and ethical data-handling requirements enables smaller centres to participate in research networks without heavy infrastructure or cross-border data transfers.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fa13bd9 elementor-widget elementor-widget-menu-anchor\" data-id=\"fa13bd9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"precisiononcology\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8b62248 elementor-widget elementor-widget-spacer\" data-id=\"8b62248\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0a651ef elementor-widget elementor-widget-image\" data-id=\"0a651ef\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.m3globalresearch.com\/blog\/wp-content\/uploads\/2025\/10\/tumour-imaging-and-genomics-scaled.jpeg\" title=\"tumour imaging ang genomics\" alt=\"tumour imaging ang genomics\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f9eba8f elementor-widget elementor-widget-spacer\" data-id=\"f9eba8f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e61fe82 elementor-widget elementor-widget-heading\" data-id=\"e61fe82\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Technical and Regulatory Challenges in Multi-Centre AI Collaboration<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d262019 elementor-widget elementor-widget-text-editor\" data-id=\"d262019\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>While the promise of federated learning in cancer research is significant, practical barriers remain before widespread clinical deployment. Among the challenges, there are:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-16d5744 elementor-widget elementor-widget-text-editor\" data-id=\"16d5744\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong>1. Data Heterogeneity and Non-IID Distributions <\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f606906 elementor-widget elementor-widget-text-editor\" data-id=\"f606906\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Each participating centre generates data using different equipment, imaging protocols and clinical workflows. These \u201cnon-independent, non-identically distributed\u201d (non-IID) datasets can cause unstable model convergence and biased outcomes. Standardising pre-processing pipelines and harmonising feature definitions are crucial to maintain comparability.<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050925003503\" target=\"_blank\" rel=\"noopener\">*<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-326bff3 elementor-widget elementor-widget-text-editor\" data-id=\"326bff3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong>2. Limited Labels and Annotation Burden <\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-62618e1 elementor-widget elementor-widget-text-editor\" data-id=\"62618e1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>High-quality labels, such as tumour boundaries, histologic subtypes, or genomic annotations, remain labour-intensive and inconsistent across sites. Without harmonised annotation guidelines, federated models risk learning false correlations. Collaborative labelling initiatives are beginning to address this gap.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f54ec87 elementor-widget elementor-widget-text-editor\" data-id=\"f54ec87\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong>3. Privacy, Security and Model Leakage <\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9a16018 elementor-widget elementor-widget-text-editor\" data-id=\"9a16018\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Although raw data are never shared, the model updates themselves can reveal patterns exploitable through \u201cmodel inversion\u201d or \u201cmembership inference\u201d attacks. Advanced encryption, secure multi-party computation, and differential privacy techniques mitigate these threats, but implementing them increases computational cost and complexity.<a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/37610908\/\" target=\"_blank\" rel=\"noopener\">*<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ffacf2c elementor-widget elementor-widget-text-editor\" data-id=\"ffacf2c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong>4. Governance and Trust <\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fec1240 elementor-widget elementor-widget-text-editor\" data-id=\"fec1240\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Successful multi-centre AI collaboration requires formal governance frameworks defining responsibilities, auditing, and accountability.<a href=\"https:\/\/digitalregulation.org\/a-guide-towards-collaborative-ai-frameworks\/\" target=\"_blank\" rel=\"noopener\">*<\/a> Among this governance are three main domains: procedural (transparent logging and reproducibility standards); relational (inter-institutional agreements and communication protocols); and structural (oversight committees ensuring compliance with ethical and legal mandates).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e064d1 elementor-widget elementor-widget-text-editor\" data-id=\"3e064d1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong>5. Infrastructure and Scalability <\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c940b59 elementor-widget elementor-widget-text-editor\" data-id=\"c940b59\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Deploying federated networks requires robust IT infrastructure, including secure communication channels, version control, and automated model orchestration. Cloud-based solutions can help, but cross-border regulatory compliance remains complex.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4b116af elementor-widget elementor-widget-menu-anchor\" data-id=\"4b116af\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"genomics\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4dd6b35 elementor-widget elementor-widget-spacer\" data-id=\"4dd6b35\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-123ed90 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"123ed90\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.m3globalresearch.com\/blog\/wp-content\/uploads\/2025\/10\/precision-oncology-scaled.jpeg\" title=\"precision oncology\" alt=\"precision oncology\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9f8fab8 elementor-widget elementor-widget-spacer\" data-id=\"9f8fab8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-76266ff elementor-widget elementor-widget-heading\" data-id=\"76266ff\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Future Prospects of Federated Learning in Oncology and Tumour Research<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4a96a74 elementor-widget elementor-widget-text-editor\" data-id=\"4a96a74\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The next decade promises a profound expansion of federated learning in cancer research, shaped by four main trends, listed below.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2b0f71 elementor-widget elementor-widget-text-editor\" data-id=\"a2b0f71\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong>1. Multimodal Federated Models<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2017328 elementor-widget elementor-widget-text-editor\" data-id=\"2017328\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Cancer is a multimodal disease. Integrating radiology, pathology, genomics, and clinical data within a federated framework could yield powerful predictive models for treatment response and survival.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d7726ce elementor-widget elementor-widget-text-editor\" data-id=\"d7726ce\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong>2. Global Consortia and Rare Cancers <\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-787df08 elementor-widget elementor-widget-text-editor\" data-id=\"787df08\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>International federated networks can bridge data gaps for rare or under-represented tumour types. Studies in rare brain tumours and paediatric cancers, for example, have shown that pooling local models improves outcome prediction when individual centres have limited cases.<a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC11368946\/\" target=\"_blank\" rel=\"noopener\">*<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a26ecab elementor-widget elementor-widget-text-editor\" data-id=\"a26ecab\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong>3. Clinical Trials and Implementation<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d57a679 elementor-widget elementor-widget-text-editor\" data-id=\"d57a679\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The transition from research to clinical practice will hinge on federated AI being integrated into prospective oncology trials. These will test not only accuracy but also impact on therapeutic decision-making and patient outcomes.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8831377 elementor-widget elementor-widget-text-editor\" data-id=\"8831377\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong>4. Ethical and Regulatory Evolution<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-26b0282 elementor-widget elementor-widget-text-editor\" data-id=\"26b0282\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Regulatory agencies are beginning to recognise federated learning frameworks as compliant with data-minimisation principles.<a href=\"https:\/\/www.edps.europa.eu\/data-protection\/our-work\/publications\/techdispatch\/2025-06-10-techdispatch-12025-federated-learning_en\" target=\"_blank\" rel=\"noopener\">*<\/a> However, formal guidance on auditing, explainability and model accountability is still evolving. Transparent model documentation, including version histories and governance reports, will be key for adoption.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-26df7af elementor-widget elementor-widget-text-editor\" data-id=\"26df7af\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong>5. Integration with Therapeutic Stratification<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3640b0e elementor-widget elementor-widget-text-editor\" data-id=\"3640b0e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Beyond diagnosis, federated models could guide treatment selection, radiotherapy planning, and adverse-event prediction. Multi-centre learning ensures that models trained on one population perform reliably in another, an essential step toward equitable AI in oncology.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-85f52cf elementor-widget elementor-widget-text-editor\" data-id=\"85f52cf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Federated learning, or distributed machine learning in oncology, represents a paradigm shift for how we conduct data-driven cancer research. It offers a practical balance between collaboration and confidentiality, allowing multi-centre AI in cancer to advance without sacrificing patient privacy.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5564a26 elementor-widget elementor-widget-text-editor\" data-id=\"5564a26\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>For oncologists, data scientists, and clinical researchers, federated learning in tumour research marks a new era of privacy-preserving machine learning \u2013 one where collaboration no longer requires compromise. As technical, ethical, and policy frameworks converge, federated learning is poised to become a cornerstone of global cancer research, enabling precision oncology that is both secure and inclusive.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-17e9fbc elementor-widget elementor-widget-text-editor\" data-id=\"17e9fbc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Has your institution explored federated or privacy-preserving machine learning in oncology? Join the discussion in the <a href=\"#comments2910\">comments section below<\/a> and tell us how your medical team is addressing the challenges of multi-centre AI collaboration.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8b61c1a elementor-share-buttons--view-icon elementor-share-buttons--skin-minimal elementor-share-buttons--shape-circle elementor-share-buttons--color-custom elementor-grid-0 elementor-widget elementor-widget-share-buttons\" data-id=\"8b61c1a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"share-buttons.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-grid\" role=\"list\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_email\" role=\"button\" tabindex=\"0\" aria-label=\"Share on email\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-envelope\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z\"><\/path><\/svg>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_whatsapp\" role=\"button\" tabindex=\"0\" aria-label=\"Share on whatsapp\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fab-whatsapp\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M380.9 97.1C339 55.1 283.2 32 223.9 32c-122.4 0-222 99.6-222 222 0 39.1 10.2 77.3 29.6 111L0 480l117.7-30.9c32.4 17.7 68.9 27 106.1 27h.1c122.3 0 224.1-99.6 224.1-222 0-59.3-25.2-115-67.1-157zm-157 341.6c-33.2 0-65.7-8.9-94-25.7l-6.7-4-69.8 18.3L72 359.2l-4.4-7c-18.5-29.4-28.2-63.3-28.2-98.2 0-101.7 82.8-184.5 184.6-184.5 49.3 0 95.6 19.2 130.4 54.1 34.8 34.9 56.2 81.2 56.1 130.5 0 101.8-84.9 184.6-186.6 184.6zm101.2-138.2c-5.5-2.8-32.8-16.2-37.9-18-5.1-1.9-8.8-2.8-12.5 2.8-3.7 5.6-14.3 18-17.6 21.8-3.2 3.7-6.5 4.2-12 1.4-32.6-16.3-54-29.1-75.5-66-5.7-9.8 5.7-9.1 16.3-30.3 1.8-3.7.9-6.9-.5-9.7-1.4-2.8-12.5-30.1-17.1-41.2-4.5-10.8-9.1-9.3-12.5-9.5-3.2-.2-6.9-.2-10.6-.2-3.7 0-9.7 1.4-14.8 6.9-5.1 5.6-19.4 19-19.4 46.3 0 27.3 19.9 53.7 22.6 57.4 2.8 3.7 39.1 59.7 94.8 83.8 35.2 15.2 49 16.5 66.6 13.9 10.7-1.6 32.8-13.4 37.4-26.4 4.6-13 4.6-24.1 3.2-26.4-1.3-2.5-5-3.9-10.5-6.6z\"><\/path><\/svg>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_linkedin\" role=\"button\" tabindex=\"0\" aria-label=\"Share on linkedin\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fab-linkedin\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_facebook\" role=\"button\" tabindex=\"0\" aria-label=\"Share on facebook\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fab-facebook\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M504 256C504 119 393 8 256 8S8 119 8 256c0 123.78 90.69 226.38 209.25 245V327.69h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.28c-30.8 0-40.41 19.12-40.41 38.73V256h68.78l-11 71.69h-57.78V501C413.31 482.38 504 379.78 504 256z\"><\/path><\/svg>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_twitter\" role=\"button\" tabindex=\"0\" aria-label=\"Share on twitter\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fab-twitter\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z\"><\/path><\/svg>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_pinterest\" role=\"button\" tabindex=\"0\" aria-label=\"Share on pinterest\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fab-pinterest\" viewBox=\"0 0 496 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M496 256c0 137-111 248-248 248-25.6 0-50.2-3.9-73.4-11.1 10.1-16.5 25.2-43.5 30.8-65 3-11.6 15.4-59 15.4-59 8.1 15.4 31.7 28.5 56.8 28.5 74.8 0 128.7-68.8 128.7-154.3 0-81.9-66.9-143.2-152.9-143.2-107 0-163.9 71.8-163.9 150.1 0 36.4 19.4 81.7 50.3 96.1 4.7 2.2 7.2 1.2 8.3-3.3.8-3.4 5-20.3 6.9-28.1.6-2.5.3-4.7-1.7-7.1-10.1-12.5-18.3-35.3-18.3-56.6 0-54.7 41.4-107.6 112-107.6 60.9 0 103.6 41.5 103.6 100.9 0 67.1-33.9 113.6-78 113.6-24.3 0-42.6-20.1-36.7-44.8 7-29.5 20.5-61.3 20.5-82.6 0-19-10.2-34.9-31.4-34.9-24.9 0-44.9 25.7-44.9 60.2 0 22 7.4 36.8 7.4 36.8s-24.5 103.8-29 123.2c-5 21.4-3 51.6-.9 71.2C65.4 450.9 0 361.1 0 256 0 119 111 8 248 8s248 111 248 248z\"><\/path><\/svg>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_xing\" role=\"button\" tabindex=\"0\" aria-label=\"Share on xing\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fab-xing\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M162.7 210c-1.8 3.3-25.2 44.4-70.1 123.5-4.9 8.3-10.8 12.5-17.7 12.5H9.8c-7.7 0-12.1-7.5-8.5-14.4l69-121.3c.2 0 .2-.1 0-.3l-43.9-75.6c-4.3-7.8.3-14.1 8.5-14.1H100c7.3 0 13.3 4.1 18 12.2l44.7 77.5zM382.6 46.1l-144 253v.3L330.2 466c3.9 7.1.2 14.1-8.5 14.1h-65.2c-7.6 0-13.6-4-18-12.2l-92.4-168.5c3.3-5.8 51.5-90.8 144.8-255.2 4.6-8.1 10.4-12.2 17.5-12.2h65.7c8 0 12.3 6.7 8.5 14.1z\"><\/path><\/svg>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a5ac5e elementor-widget elementor-widget-spacer\" data-id=\"5a5ac5e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-645a8b6 e-flex e-con-boxed e-con e-child\" data-id=\"645a8b6\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-5a1a3ac e-con-full e-flex e-con e-child\" data-id=\"5a1a3ac\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-24b2003 elementor-widget elementor-widget-spacer\" data-id=\"24b2003\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-48fce86 e-flex e-con-boxed e-con e-child\" data-id=\"48fce86\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e9d19b0 elementor-widget elementor-widget-heading\" data-id=\"e9d19b0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Are You a Healthcare Professional?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cfc7cad elementor-widget elementor-widget-text-editor\" data-id=\"cfc7cad\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong>Did you enjoy this article? <br \/>Join our community of healthcare professionals to share your opinion<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-036fec4 e-flex e-con-boxed e-con e-child\" data-id=\"036fec4\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-cb7c8a0 elementor-widget elementor-widget-spacer\" data-id=\"cb7c8a0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a5762d9 elementor-widget elementor-widget-spacer\" data-id=\"a5762d9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-37c9806 elementor-widget elementor-widget-heading\" data-id=\"37c9806\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">You Might Be Interested in:<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aea15b0 elementor-widget elementor-widget-menu-anchor\" data-id=\"aea15b0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"commentdementia\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9413104 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile e-flex e-con-boxed e-con e-child\" data-id=\"9413104\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-0fec266 e-con-full e-flex e-con e-child\" data-id=\"0fec266\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-db3de88 elementor-widget elementor-widget-spacer\" data-id=\"db3de88\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-230397e e-flex e-con-boxed e-con e-child\" data-id=\"230397e\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-02208ff elementor-widget elementor-widget-heading\" data-id=\"02208ff\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Are You an Oncologist or Haematologist?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2227840 elementor-widget elementor-widget-text-editor\" data-id=\"2227840\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>What is your opinion on new technologies and advancements in chemotherapy,\u00a0 hormone therapy, immunotherapy, and targeted therapy? Join M3 today and share your opinion<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ba5c953 e-flex e-con-boxed e-con e-child\" data-id=\"ba5c953\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4b8845a elementor-widget elementor-widget-spacer\" data-id=\"4b8845a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9485f03 e-flex e-con-boxed e-con e-child\" data-id=\"9485f03\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b476f7c elementor-align-center elementor-widget elementor-widget-button\" data-id=\"b476f7c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/m3globalresearch.blog\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">M3 Blog Index<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-198ffe7 e-flex e-con-boxed e-con e-child\" data-id=\"198ffe7\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c540dad elementor-widget elementor-widget-spacer\" data-id=\"c540dad\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-834740b elementor-widget elementor-widget-menu-anchor\" data-id=\"834740b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"comments2910\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>Explore tumour imaging, genomics, and federated learning in cancer research to advance precision oncology and personalized cancer treatment.<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":165244,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[8134,8201],"tags":[8263,8264,8265,8266],"class_list":["post-165231","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-oncology-focus","category-physician-hub","tag-federated-learning","tag-genomics","tag-precision-oncology","tag-tumour-imaging"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.6 (Yoast SEO v27.7) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Federated Learning in Cancer Research<\/title>\n<meta name=\"description\" content=\"Explore tumour imaging, genomics, and federated learning in cancer research to advance precision oncology and personalised cancer treatment.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Federated Learning in Cancer Research: Enabling Multi-Centre AI While Preserving Patient Privacy\" \/>\n<meta property=\"og:description\" content=\"Explore tumour imaging, genomics, and federated learning in cancer research to advance precision oncology and personalised cancer treatment.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/\" \/>\n<meta property=\"og:site_name\" content=\"M3 Global Research\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/en-gb.facebook.com\/M3GlobalResearch\/\" \/>\n<meta property=\"article:published_time\" content=\"2025-10-29T10:24:25+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.m3globalresearch.com\/blog\/wp-content\/uploads\/2025\/10\/federated-learning-in-cancer-research-1024x683.jpeg\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"683\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"m3gr-blog-admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@M3GR_Community\" \/>\n<meta name=\"twitter:site\" content=\"@M3GR_Community\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"m3gr-blog-admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"8 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/\"},\"author\":{\"name\":\"m3gr-blog-admin\",\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/#\\\/schema\\\/person\\\/11bace963d7a45380011c4911a822a6c\"},\"headline\":\"Federated Learning in Cancer Research: Enabling Multi-Centre AI While Preserving Patient Privacy\",\"datePublished\":\"2025-10-29T10:24:25+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/\"},\"wordCount\":1386,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/wp-content\\\/uploads\\\/2025\\\/10\\\/federated-learning-in-cancer-research-scaled.jpeg\",\"keywords\":[\"Federated learning\",\"genomics\",\"precision oncology\",\"tumour imaging\"],\"articleSection\":[\"Oncology Focus\",\"Physician Hub\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/#respond\"]}]},{\"@type\":[\"WebPage\",\"MedicalWebPage\"],\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/\",\"url\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/\",\"name\":\"Federated Learning in Cancer Research\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/wp-content\\\/uploads\\\/2025\\\/10\\\/federated-learning-in-cancer-research-scaled.jpeg\",\"datePublished\":\"2025-10-29T10:24:25+00:00\",\"description\":\"Explore tumour imaging, genomics, and federated learning in cancer research to advance precision oncology and personalised cancer treatment.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/#primaryimage\",\"url\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/wp-content\\\/uploads\\\/2025\\\/10\\\/federated-learning-in-cancer-research-scaled.jpeg\",\"contentUrl\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/wp-content\\\/uploads\\\/2025\\\/10\\\/federated-learning-in-cancer-research-scaled.jpeg\",\"width\":4992,\"height\":3328,\"caption\":\"Explore tumour imaging, genomics, and federated learning in cancer research to advance precision oncology and personalised cancer treatment.\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/federated-learning-cancer-research\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Federated Learning in Cancer Research: Enabling Multi-Centre AI While Preserving Patient Privacy\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/#website\",\"url\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/\",\"name\":\"M3 Global Research\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/#organization\",\"name\":\"M3 Global Research\",\"url\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":null,\"contentUrl\":null,\"width\":null,\"height\":null,\"caption\":\"M3 Global Research\"},\"image\":{\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/en-gb.facebook.com\\\/M3GlobalResearch\\\/\",\"https:\\\/\\\/x.com\\\/M3GR_Community\",\"https:\\\/\\\/www.instagram.com\\\/m3globalresearch\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/10557915\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/#\\\/schema\\\/person\\\/11bace963d7a45380011c4911a822a6c\",\"name\":\"m3gr-blog-admin\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/fb0c444d9d8b383c405133f87af6215220f0327b7f797aec37db4ab6b6b18364?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/fb0c444d9d8b383c405133f87af6215220f0327b7f797aec37db4ab6b6b18364?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/fb0c444d9d8b383c405133f87af6215220f0327b7f797aec37db4ab6b6b18364?s=96&d=mm&r=g\",\"caption\":\"m3gr-blog-admin\"},\"sameAs\":[\"https:\\\/\\\/blog.m3globalresearch.com\"],\"url\":\"https:\\\/\\\/www.m3globalresearch.com\\\/blog\\\/author\\\/m3gr-blog-admin\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Federated Learning in Cancer Research","description":"Explore tumour imaging, genomics, and federated learning in cancer research to advance precision oncology and personalised cancer treatment.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/","og_locale":"en_US","og_type":"article","og_title":"Federated Learning in Cancer Research: Enabling Multi-Centre AI While Preserving Patient Privacy","og_description":"Explore tumour imaging, genomics, and federated learning in cancer research to advance precision oncology and personalised cancer treatment.","og_url":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/","og_site_name":"M3 Global Research","article_publisher":"https:\/\/en-gb.facebook.com\/M3GlobalResearch\/","article_published_time":"2025-10-29T10:24:25+00:00","og_image":[{"width":1024,"height":683,"url":"https:\/\/www.m3globalresearch.com\/blog\/wp-content\/uploads\/2025\/10\/federated-learning-in-cancer-research-1024x683.jpeg","type":"image\/jpeg"}],"author":"m3gr-blog-admin","twitter_card":"summary_large_image","twitter_creator":"@M3GR_Community","twitter_site":"@M3GR_Community","twitter_misc":{"Written by":"m3gr-blog-admin","Est. reading time":"8 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/#article","isPartOf":{"@id":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/"},"author":{"name":"m3gr-blog-admin","@id":"https:\/\/www.m3globalresearch.com\/blog\/#\/schema\/person\/11bace963d7a45380011c4911a822a6c"},"headline":"Federated Learning in Cancer Research: Enabling Multi-Centre AI While Preserving Patient Privacy","datePublished":"2025-10-29T10:24:25+00:00","mainEntityOfPage":{"@id":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/"},"wordCount":1386,"commentCount":0,"publisher":{"@id":"https:\/\/www.m3globalresearch.com\/blog\/#organization"},"image":{"@id":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/#primaryimage"},"thumbnailUrl":"https:\/\/www.m3globalresearch.com\/blog\/wp-content\/uploads\/2025\/10\/federated-learning-in-cancer-research-scaled.jpeg","keywords":["Federated learning","genomics","precision oncology","tumour imaging"],"articleSection":["Oncology Focus","Physician Hub"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/#respond"]}]},{"@type":["WebPage","MedicalWebPage"],"@id":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/","url":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/","name":"Federated Learning in Cancer Research","isPartOf":{"@id":"https:\/\/www.m3globalresearch.com\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/#primaryimage"},"image":{"@id":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/#primaryimage"},"thumbnailUrl":"https:\/\/www.m3globalresearch.com\/blog\/wp-content\/uploads\/2025\/10\/federated-learning-in-cancer-research-scaled.jpeg","datePublished":"2025-10-29T10:24:25+00:00","description":"Explore tumour imaging, genomics, and federated learning in cancer research to advance precision oncology and personalised cancer treatment.","breadcrumb":{"@id":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/#primaryimage","url":"https:\/\/www.m3globalresearch.com\/blog\/wp-content\/uploads\/2025\/10\/federated-learning-in-cancer-research-scaled.jpeg","contentUrl":"https:\/\/www.m3globalresearch.com\/blog\/wp-content\/uploads\/2025\/10\/federated-learning-in-cancer-research-scaled.jpeg","width":4992,"height":3328,"caption":"Explore tumour imaging, genomics, and federated learning in cancer research to advance precision oncology and personalised cancer treatment."},{"@type":"BreadcrumbList","@id":"https:\/\/www.m3globalresearch.com\/blog\/federated-learning-cancer-research\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.m3globalresearch.com\/blog\/"},{"@type":"ListItem","position":2,"name":"Federated Learning in Cancer Research: Enabling Multi-Centre AI While Preserving Patient Privacy"}]},{"@type":"WebSite","@id":"https:\/\/www.m3globalresearch.com\/blog\/#website","url":"https:\/\/www.m3globalresearch.com\/blog\/","name":"M3 Global Research","description":"","publisher":{"@id":"https:\/\/www.m3globalresearch.com\/blog\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.m3globalresearch.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.m3globalresearch.com\/blog\/#organization","name":"M3 Global Research","url":"https:\/\/www.m3globalresearch.com\/blog\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.m3globalresearch.com\/blog\/#\/schema\/logo\/image\/","url":null,"contentUrl":null,"width":null,"height":null,"caption":"M3 Global Research"},"image":{"@id":"https:\/\/www.m3globalresearch.com\/blog\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/en-gb.facebook.com\/M3GlobalResearch\/","https:\/\/x.com\/M3GR_Community","https:\/\/www.instagram.com\/m3globalresearch","https:\/\/www.linkedin.com\/company\/10557915"]},{"@type":"Person","@id":"https:\/\/www.m3globalresearch.com\/blog\/#\/schema\/person\/11bace963d7a45380011c4911a822a6c","name":"m3gr-blog-admin","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/fb0c444d9d8b383c405133f87af6215220f0327b7f797aec37db4ab6b6b18364?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/fb0c444d9d8b383c405133f87af6215220f0327b7f797aec37db4ab6b6b18364?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/fb0c444d9d8b383c405133f87af6215220f0327b7f797aec37db4ab6b6b18364?s=96&d=mm&r=g","caption":"m3gr-blog-admin"},"sameAs":["https:\/\/blog.m3globalresearch.com"],"url":"https:\/\/www.m3globalresearch.com\/blog\/author\/m3gr-blog-admin\/"}]}},"jetpack_featured_media_url":"https:\/\/www.m3globalresearch.com\/blog\/wp-content\/uploads\/2025\/10\/federated-learning-in-cancer-research-scaled.jpeg","jetpack_sharing_enabled":true,"gt_translate_keys":[{"key":"link","format":"url"}],"_links":{"self":[{"href":"https:\/\/www.m3globalresearch.com\/blog\/wp-json\/wp\/v2\/posts\/165231","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.m3globalresearch.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.m3globalresearch.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.m3globalresearch.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.m3globalresearch.com\/blog\/wp-json\/wp\/v2\/comments?post=165231"}],"version-history":[{"count":0,"href":"https:\/\/www.m3globalresearch.com\/blog\/wp-json\/wp\/v2\/posts\/165231\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.m3globalresearch.com\/blog\/wp-json\/wp\/v2\/media\/165244"}],"wp:attachment":[{"href":"https:\/\/www.m3globalresearch.com\/blog\/wp-json\/wp\/v2\/media?parent=165231"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.m3globalresearch.com\/blog\/wp-json\/wp\/v2\/categories?post=165231"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.m3globalresearch.com\/blog\/wp-json\/wp\/v2\/tags?post=165231"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}