Advancing Multi-Center Cancer Research Through Federated Learning: Privacy-Preserving Artificial Intelligence in Oncology

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide, underscoring the urgent need for advanced computational approaches that can improve cancer diagnosis, prognosis, and personalized treatment. Artificial intelligence (AI) has emerged as a transformative technology in oncology, enabling automated medical image interpretation, biomarker discovery, treatment response prediction, and integration of multimodal biomedical data. However, most conventional AI models rely on centralized data aggregation, requiring patient information from multiple institutions to be transferred to a central repository for model development. Such approaches raise significant concerns regarding patient privacy, data ownership, regulatory compliance, cybersecurity, and institutional data-sharing restrictions, thereby limiting large-scale collaborative cancer research. Federated learning (FL) has emerged as a decentralized, privacy-preserving AI paradigm that enables multiple healthcare institutions to collaboratively train machine learning models without exchanging raw patient data. By keeping sensitive data within local institutions and sharing only encrypted model parameters or updates, FL facilitates secure collaboration while maintaining data confidentiality. In oncology, FL has demonstrated considerable potential for distributed analysis of medical imaging, genomic and multi-omics data, electronic health records, digital pathology, and clinical trial datasets, thereby supporting improved cancer detection, tumor characterization, disease progression modeling, biomarker discovery, and personalized therapeutic decision-making. Despite these advantages, widespread implementation of FL in oncology remains challenged by data heterogeneity, non-identically distributed datasets, communication overhead, computational resource variability, model bias, cybersecurity threats, and the absence of standardized validation and regulatory frameworks. Future integration of FL with deep learning, foundation models, generative artificial intelligence, multimodal learning, multi-omics analytics, and international cancer research networks is expected to accelerate the development of secure, collaborative, and privacy-preserving precision oncology platforms. This review provides a comprehensive overview of federated learning principles, system architectures, security mechanisms, clinical applications, current challenges, and future research directions, highlighting its growing role in enabling privacy-preserving artificial intelligence for multi-center cancer research.oncology practice.