Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach

arXiv:2402.18018v1 Announce Type: new
Abstract: Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data dispersed over various data sources. Standard FL, which employs a single server, can only support a limited number of users, leading to degraded learning capability. In this work, we consider a multi-server FL framework, referred to as \emph{Confederated Learning} (CFL), in order to accommodate a larger number of users. A CFL system is composed of multiple networked edge …

abstract arxiv capability communication cs.dc cs.lg data data sources eess.sp event federated learning machine machine learning paradigm saga server standard support targets training type work