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-rich institutions. As a result, resource-limited institutions may receive suboptimal models, further reinforcing disparities in AI-driven healthcare outcomes. Here, we propose a resource-adaptive framework for collaborative learning that dynamically adjusts to varying computational capacities, ensuring fair participation. Our approach enhances model accuracy, safeguards patient privacy, and promotes equitable access to trustworthy and efficient AI-driven healthcare solutions.