Learning from straggler clients in federated learning

arXiv:2403.09086v1 Announce Type: new
Abstract: How well do existing federated learning algorithms learn from client devices that return model updates with a significant time delay? Is it even possible to learn effectively from clients that report back minutes, hours, or days after being scheduled? We answer these questions by developing Monte Carlo simulations of client latency that are guided by real-world applications. We study synchronous optimization algorithms like FedAvg and FedAdam as well as the asynchronous FedBuff algorithm, and observe …

abstract algorithms arxiv client cs.lg delay devices federated learning learn questions report simulations type updates