Hermes: Fast Semi-Asynchronous Federated Learning in LEO Constellations

Yan Chen, Jun Liu, Jiejie Zhao, Guanjun Jiang, Haiquan Wang, Bowen Du

Abstract: Recent advances in space technology have prompted the emergence of numerous Low Earth Orbit (LEO) satellites, producing mega-constellations that can pro-vide global network coverage and collect massive distributed data, bringing new opportunities for intelligence applications to remote areas. To achieve such goals, Federated Learning (FL) is a promising solution to train the global model over LEO satellites and ground station networks while reducing the high communication cost caused by data exchanging. However, the widely used synchronous FL may face intol-erable waiting time due to the intermittent connectivity. In addition, existing asynchronous FL suffers from model staleness attributed to asynchronous training, which may decrease the performance of the global model. To this end, we propose a novel semi-asynchronous federated learning frame-work in LEO constellations, namely Hermes, that includes a latency-aware model delivery mechanism and an adaptive semi-asynchronous aggregation algorithm to improve the convergence rate and generality of the global model. Our simulation results show that Hermes outperforms existing LEO-based FL methods across various constellation scales, achieving higher model accuracy with average speedups of 3.29x and 7.26x for MNIST and EMNIST, respectively.