One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity

arXiv:2404.12130v1 Announce Type: new
Abstract: Traditional federated learning mainly focuses on parallel settings (PFL), which can suffer significant communication and computation costs. In contrast, one-shot and sequential federated learning (SFL) have emerged as innovative paradigms to alleviate these costs. However, the issue of non-IID (Independent and Identically Distributed) data persists as a significant challenge in one-shot and SFL settings, exacerbated by the restricted communication between clients. In this paper, we improve the one-shot sequential federated learning for non-IID data by …

abstract arxiv communication computation contrast costs cs.cv cs.dc cs.lg data distributed diversity federated learning however independent issue type