CANCER INTELLIGENCE NETWORKS: FEDERATED FOUNDATION MODELS FOR SECURE AND COLLABORATIVE PRECISION ONCOLOGY

Abstract

Cancer intelligence networks are emerging as a transformative paradigm in precision oncology by combining federated foundation artificial intelligence (AI) models, distributed biomedical intelligence, and privacy-preserving computational infrastructures to enable secure collaboration across healthcare institutions. Conventional AI development in oncology is frequently constrained by fragmented biomedical datasets, institutional data silos, regulatory restrictions, patient privacy concerns, and limited generalizability of locally trained algorithms. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, federated learning, reinforcement learning, and generative artificial intelligence have enabled decentralized integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes without direct exchange of sensitive patient information. These intelligent systems support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, digital twin simulation, adaptive clinical trials, knowledge graph construction, and evidence-based clinical decision support while preserving institutional autonomy and patient confidentiality. Emerging technologies including multimodal large language models, agentic AI, retrieval-augmented generation, explainable artificial intelligence, cloud-native healthcare platforms, differential privacy, secure multi-party computation, and digital health ecosystems further strengthen collaborative precision oncology by enabling trustworthy, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, regulatory, and implementation challenges remain regarding interoperability, computational scalability, cybersecurity, model harmonization, governance, fairness, and equitable deployment. This review provides a comprehensive overview of cancer intelligence networks, emphasizing federated foundation models for secure and collaborative precision oncology.