Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare.

ABSTRACT: The widespread integration of AI algorithms in healthcare has sparked ethical concerns, particularly regarding privacy and fairness. Federated Learning (FL) offers a promising solution to learn from a broad spectrum of patient data without directly accessing individual records, enhancing privacy while facilitating knowledge sharing across distributed data sources. However, healthcare institutions face significant variations in access to crucial computing resources, with resource budgets often linked to demographic and socio-economic factors, exacerbating unfairness in participation. While heterogeneous federated learning methods allow healthcare institutions with varying computational capacities to collaborate, they fail to address the performance gap between resource-limited and resource-

SUBMITTER: Zhang F 

PROVIDER: S-EPMC11930927 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature