Joint Server Selection and Handover Design for Satellite-Based Federated Learning Using Mean-Field Evolutionary Approach

With the increasing development of Low-Earth-Orbit (LEO) Satelite Communications (SatComs), it is foreseen that they will play an important role in broadening the horizon of Federated Learning (FL). Specifically, SatComs can amplify FL by providing consistent global transmission, bridging terrestrial network gaps, and ensuring robust, reliable connectivity in remote or challenging terrains. In this paper, we consider a SatComs-based FL framework, where satellites in the low-earth orbit collaborate to serve as global servers, able to collect and aggregate FL model parameters transmitted from the mobile devices on the ground continuously. We investigate the joint server selection and handover design optimization problem for SatComs-based FL from the perspective of the system's energy consumption and performance. To address the scalability issue, we propose a Mean-Field-Evolutionary (MFEv) approach that simplifies the interaction between mobile devices as a distribution over their state space, known as the mean-field approximation. It iteratively updates devices' strategies along the Fokker-Planck gradient flow in satellites' strategy space. Our approach's complexity is linear in the number of mobile devices, and we prove that it converges to a unique optimal solution. Numerical simulations demonstrate that our approach is effective in economically benefiting the system and reducing algorithm running time.