CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning

Federated learning (FL) is a distributed optimization paradigm that learns from data samples distributed across a number of clients. Adaptive client selection that is cognizant of the training progress of clients has become a major trend to improve FL efficiency but not yet well-understood. Most existing FL methods such as FedAvg and its state-of-the-art variants implicitly assume that all learning phases during the FL training process are equally important. Unfortunately, this assumption has been revealed to be invalid due to recent findings on critical learning periods (CLP), in which small gradient errors may lead to an irrecoverable deficiency on final test accuracy. In this paper, we develop CriticalFL, a CLP augmented FL framework to reveal that adaptively augmenting exiting FL methods with CLP, the resultant performance is significantly improved when the client selection is guided by the discovered CLP. Experiments based on various machine learning models and datasets validate that the proposed CriticalFL framework consistently achieves an improved model accuracy while maintains better communication efficiency as compared to state-of-the-art methods, demonstrating a promising and easily adopted method for tackling the heterogeneity of FL training.