MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates

arXiv:2306.12212v4 Announce Type: replace
Abstract: Federated learning (FL) is a promising framework for privacy-preserving collaborative learning, where model training tasks are distributed to clients and only the model updates need to be collected at a server. However, when being deployed at mobile edge networks, clients may have unpredictable availability and drop out of the training process, which hinders the convergence of FL. This paper tackles such a critical challenge. Specifically, we first investigate the convergence of the classical FedAvg algorithm …

abstract arxiv availability client collaborative cs.dc cs.lg distributed edge edge networks federated learning framework however mobile networks privacy server tasks training type updates