Convergence Analysis of Sequential Federated Learning on Heterogeneous Data

arXiv:2311.03154v2 Announce Type: replace
Abstract: There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: i) parallel FL (PFL), where clients train models in a parallel manner; and ii) sequential FL (SFL), where clients train models in a sequential manner. In contrast to that of PFL, the convergence theory of SFL on heterogeneous data is still lacking. In this paper, we establish the convergence guarantees of SFL for strongly/general/non-convex objectives on heterogeneous data. The …

abstract analysis arxiv contrast convergence cs.lg data federated learning multiple train training type