Federated and Transfer Learning Approaches for Data-Scarce Healthcare Applications

Abstract

Healthcare machine learning systems frequently operate under severe data constraints caused by privacy regulations, limited patient cohorts, and costly annotation processes. Federated learning and transfer learning offer complementary strategies to address these challenges by enabling knowledge sharing without centralized data aggregation and by reusing learned representations across tasks and domains. This article investigates the combined role of federated and transfer learning in data-scarce healthcare applications. We analyze how these approaches improve model generalization, reduce privacy risk, and enhance robustness across heterogeneous clinical environments. An integrated architectural framework is proposed and empirically evaluated across representative healthcare scenarios. Results demonstrate that federated and transfer learning can substantially improve predictive performance while preserving data locality and reducing reliance on large labeled datasets.