Improved Generalization Bounds for Communication Efficient Federated Learning

arXiv:2404.11754v1 Announce Type: new
Abstract: This paper focuses on reducing the communication cost of federated learning by exploring generalization bounds and representation learning. We first characterize a tighter generalization bound for one-round federated learning based on local clients' generalizations and heterogeneity of data distribution (non-iid scenario). We also characterize a generalization bound in R-round federated learning and its relation to the number of local updates (local stochastic gradient descents (SGDs)). Then, based on our generalization bound analysis and our representation …

abstract arxiv communication cost cs.ai cs.lg data distribution federated learning paper representation representation learning type