Decentralized Federated Learning Over Imperfect Communication Channels
Decentralized Federated Learning Over Imperfect Communication Channels
This paper analyzes the impact of imperfect communication channels on decentralized federated learning (D-FL) and subsequently determines the optimal number of local aggregations per training round, adapting to the network topology and imperfect channels. We start by deriving the bias of locally aggregated D-FL models under imperfect channels from the …