Auction-Driven Utility Maximization for Multiple Federated Learning in Smart Healthcare

Federated Learning (FL), a decentralized Machine Learning (ML) approach, allows Wireless Body Area Network (WBAN) users to collaboratively train models while maintaining the privacy of their health data. With the rise of ML-powered smart healthcare applications and increasing demand for diverse services, the simultaneous training of multiple FL models using data from WBANs is becoming feasible. However, managing multiple FL models with distinct learning objectives presents challenges in designing incentives and selecting suitable users—an area that remains largely unexplored. Thus, this paper introduces an auction-based incentive mechanism and WBAN users selection framework to enable the parallel training of multiple FL models, ensuring privacy of data. An optimization problem is formulated to maximize system utility, incorporating a cost model that includes data collection, computation, communication, and privacy. The proposed auction-based algorithm integrates factors such as local model accuracy, user reputation, and data volume to solve this problem efficiently. Simulations and real-world health data analysis demonstrate that this approach improves average utility by 15.9% and 18.08% compared to state-of-the-art methods.