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 9\% improvement in FEMINST test accuracy. Moreover, GPFL exhibits shorter computation times through pre-selection and parameter reuse in federated learning.

翻译：联邦学习客户端选择对于在平衡模型精度与通信效率的同时确定参与客户端至关重要。现有方法在处理数据异构性、计算负担以及客户端独立处理方面存在局限。为应对这些挑战，我们提出GPFL框架，该框架通过比较局部与全局下降方向来衡量客户端价值。我们还采用利用-探索机制以提升性能。在FEMNIST和CIFAR-10数据集上的实验结果表明，GPFL在非独立同分布场景中优于基线方法，在FEMNIST测试准确率上实现超过9%的提升。此外，GPFL通过预选择机制和联邦学习中的参数复用，展现出更短的计算时间。