Efficient Federated Learning in 6G-Satellite Systems: Deep Reinforcement Learning Based Multi-Objective Optimization

Wireless-based federated learning (FL), as an emerging distributed learning approach, has been widely studied for 6G systems. When the paradigm shifts from terrestrial to non-terrestrial networks (NTN), FL may need to address several open challengings, e.g., limited service time of low earth orbit (LEO) satellites and time-efficient uploading and aggregation for massive devices. In this work, we exploit the synergy of LEO and FL for future integrated 6G-satellite systems by taking advantage of ubiquitous wireless access provided by LEO and appealing characteristics of collaborative training and data privacy preservation in FL. The studied LEO-FL framework may need to improve multi-metric performance in practice. Different from most FL works, we simultaneously improve the communication-training efficiency and local training accuracy from a multi-objective optimization (MOO) perspective. To solve the problem, we propose a decomposition, deep reinforcement learning and transfer learning based MOO algorithm for FL (DRT-FL), aiming at adapting to the dynamic satellite-terrestrial environments, achieving efficient uploading and aggregation, and approaching Pareto optimal sets. Compared to the state-of-the-art MOO algorithms, the effectiveness of the proposed LEO-FL framework and DRT-FL algorithm are assessed on MNIST and CIFAR-IO datasets.