Federated Foundation Models for Privacy-Preserving Precision Cancer Intelligence Across Healthcare Systems

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular biology, targeted therapeutics, immunotherapy, and precision medicine. The increasing availability of multimodal biomedical data generated through radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory investigations, wearable technologies, and electronic health records has created unprecedented opportunities for artificial intelligence (AI)-driven precision oncology. However, widespread implementation of large-scale AI models is constrained by strict privacy regulations, institutional data silos, heterogeneous healthcare infrastructures, and concerns regarding secure sharing of sensitive patient information. Federated foundation models have emerged as a transformative computational paradigm that combines federated learning with large multimodal foundation models to enable collaborative model development without transferring raw patient data across institutions. These systems facilitate privacypreserving representation learning while integrating imaging, pathology, multi-omics, clinical documentation, longitudinal patient records, and real-world healthcare data into generalized biomedical intelligence capable of supporting diagnosis, prognostic prediction, therapeutic optimization, clinical decision support, and precision medicine. Advances in transformer architectures, self-supervised learning, multimodal foundation models, graph neural networks, differential privacy, secure aggregation, homomorphic encryption, blockchain technologies, and cloud-native healthcare infrastructure have significantly accelerated development of federated computational oncology ecosystems. Despite remarkable progress, important challenges remain regarding interoperability, communication efficiency, computational scalability, model heterogeneity, explainability, regulatory validation, cybersecurity, and ethical governance. This review presents a comprehensive overview of federated foundation models in precision oncology, highlighting computational principles, clinical opportunities, implementation challenges, and future perspectives for privacy-preserving cancer intelligence across distributed healthcare systems.