Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity

arXiv:2308.01562v3 Announce Type: replace-cross
Abstract: While a practical wireless network has many tiers where end users do not directly communicate with the central server, the users' devices have limited computation and battery powers, and the serving base station (BS) has a fixed bandwidth. Owing to these practical constraints and system models, this paper leverages model pruning and proposes a pruning-enabled hierarchical federated learning (PHFL) in heterogeneous networks (HetNets). We first derive an upper bound of the convergence rate that clearly …

abstract arxiv bandwidth battery computation cs.lg cs.ni cs.sy devices eess.sy end users federated learning hierarchical network networks practical pruning server type wireless