Hierarchical Asynchronous Federated Learning over Space-Air-Ground Integrated Networks
Kai Wang, Chee Wei Tan
Abstract
With the emerging 6G paradigm where AI services operate over decentralized and resource-constrained edge infrastructure, performance of distributed learning algorithms remains a significant challenge. This paper proposes HAFL, a Low Earth Orbit (LEO) satellite-assisted hierarchical asynchronous Federated Learning (FL) framework, enabling resource-limited terrestrial clients to collaborate via Ground-Satellite Links (GSLs). Within this framework, LEO satellites serve multiple roles: as edge servers aggregating and broadcasting models, as clients accelerating training, and as high-throughput relays managing inter-satellite routing through Inter-Satellite Links (ISLs). Unlike conventional synchronous Jacobian-type updates, HAFL employs asynchronous Gauss-Seidel-type updates to enhance convergence speed. We provide theoretical convergences for convex, strongly convex, and nonconvex objectives, respectively, and formulate optimization problems to jointly determine optimal hyperparameters to minimize global training latency. Evaluations demonstrate that HAFL achieves faster initial convergence and comparable final performance for benchmark DNNs and datasets, offering promising FL systems across remote areas with 6G technologies, but much less global rounds and communication cost.