Federated Learning for Early Cancer Detection: Opportunities, Challenges, and Future Directions

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Early detection of cancer remains one of the most effective strategies to reduce cancer mortality. Artificial intelligence (AI) can enhance existing cancer screening across modalities by improving testing performance (e.g., accuracy, sensitivity, specificity), reducing false positives and negatives, automating workflows, and enabling risk-based care. However, accurate, robust, generalizable AI requires large, diverse, well-annotated datasets that are difficult to centralize because of privacy regulations, institutional governance, and heterogeneous infrastructure. Federated learning (FL) addresses this constraint by training models across institutions without transferring patient data. Through secure aggregation and differential privacy, FL preserves confidentiality while enabling collaboration. Early studies in breast, lung, and cervical screening report that FL can approach centralized performance and improve cross-site generalization; however, most rely on simulated clients, few participating sites, and limited external validation, so these findings should be regarded as preliminary rather than established. Critically, FL is not a shortcut to rigorous results, and heterogeneous, variably curated data demand intensive cleaning and quality control, which are central barriers to reliable implementation. In this review, we synthesize the state of FL for early cancer detection across imaging, digital pathology, multi-omics, and clinical data; outline core FL concepts and privacy technologies; and summarize applications by cancer type. We critically examine regulatory, operational, and translational barriers that currently limit clinical adoption. Finally, we propose a practical roadmap, which is centered on multimodal and longitudinal FL, foundation-model-enabled adaptation, prospective evaluation within clinical workflows, and equitable participation of community and low-resource sites, to advance privacy-preserving, generalizable AI for population-scale cancer screening.