Sharp Bounds for Sequential Federated Learning on Heterogeneous Data

arXiv:2405.01142v1 Announce Type: new
Abstract: There are two paradigms in Federated Learning (FL): parallel FL (PFL), where models are trained in a parallel manner across clients; and sequential FL (SFL), where models are trained in a sequential manner across clients. In contrast to that of PFL, the convergence theory of SFL on heterogeneous data is still lacking. To resolve the theoretical dilemma of SFL, we establish sharp convergence guarantees for SFL on heterogeneous data with both upper and lower bounds. …

abstract arxiv contrast convergence cs.lg data federated learning theory type