Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics

arXiv:2404.10091v1 Announce Type: cross
Abstract: Federated learning is a popular distributed learning approach for training a machine learning model without disclosing raw data. It consists of a parameter server and a possibly large collection of clients (e.g., in cross-device federated learning) that may operate in congested and changing environments. In this paper, we study federated learning in the presence of stochastic and dynamic communication failures wherein the uplink between the parameter server and client $i$ is on with unknown probability …

abstract arxiv collection cs.dc cs.lg data distributed distributed learning dynamics federated learning machine machine learning machine learning model popular raw server training type