GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning

arXiv:2403.17833v1 Announce Type: new
Abstract: Federated learning client selection is crucial for determining participant clients while balancing model accuracy and communication efficiency. Existing methods have limitations in handling data heterogeneity, computational burdens, and independent client treatment. To address these challenges, we propose GPFL, which measures client value by comparing local and global descent directions. We also employ an Exploit-Explore mechanism to enhance performance. Experimental results on FEMINST and CIFAR-10 datasets demonstrate that GPFL outperforms baselines in Non-IID scenarios, achieving over …

abstract accuracy arxiv challenges client communication computational cs.dc cs.lg data efficiency federated learning framework gradient independent limitations model accuracy projection treatment type value