May 14, 2024, 4:42 a.m. | Mahdi Morafah, Matthias Reisser, Bill Lin, Christos Louizos

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.07925v1 Announce Type: new
Abstract: The proliferation of edge devices has brought Federated Learning (FL) to the forefront as a promising paradigm for decentralized and collaborative model training while preserving the privacy of clients' data. However, FL struggles with a significant performance reduction and poor convergence when confronted with Non-Independent and Identically Distributed (Non-IID) data distributions among participating clients. While previous efforts, such as client drift mitigation and advanced server-side model fusion techniques, have shown some success in addressing this …

abstract arxiv augmentation collaborative convergence cs.ai cs.dc cs.lg data decentralized devices diffusion edge edge devices federated learning however independent paradigm performance privacy stable diffusion training type while

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