Stable Matching based Revenue Maximization for Federated Learning in UAV-Assisted WBANs

This work explores the coupling of Machine Learning (ML) and Wireless Body Area Network (WBAN) data to develop highly effective models. To support resource-constrained WBANs, we propose the integration of Drones-as-a-Service (DaaS) for on-demand data collection and model training. However, the growing number of WBAN users with varying 5G radio resources may cause interference and degrade system performance when transmitting data to Unmanned Aerial Vehicles (UAVs), hindering data sharing among independent UAVs. To address these challenges and enable privacy-preserving collaborative ML, we adopt Federated Learning (FL) framework, enabling independent UAV service providers to collaborate without sharing sensitive data. Furthermore, we aim to maximize the revenue of both WBANs, which contribute data, and UAVs, which perform model training. This requires careful resource allocation, considering minimum and maximum Physical Resource Block (PRB) requirements for transmitting critical and complete physiological data to UAVs underlying 5G networks. To tackle this complex problem, we propose an optimization framework that maximizes overall revenue while considering interference among WBANs. We apply stable matching and graph coloring-based heuristics to solve the problem efficiently. Extensive simulations and real-world data prototype demonstrate our proposed model's effectiveness, achieving an average revenue of 92.8% of the optimal value, outperforming existing state-of-the-art approaches.