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.

中文翻译：

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GPFL：基于梯度投影的高效联邦学习客户端选择框架

联邦学习客户端选择对于确定参与客户端同时平衡模型准确性和通信效率至关重要。现有方法在处理数据异质性、计算负担和独立客户处理方面存在局限性。为了应对这些挑战，我们提出了 GPFL，它通过比较本地和全球下降方向来衡量客户价值。我们还采用利用-探索机制来提高性能。 FEMINST 和 CIFAR-10 数据集上的实验结果表明，GPFL 在非 IID 场景中优于基线，在 FEMINST 测试精度方面实现了超过 9% 的改进。此外，GPFL 通过联邦学习中的预选择和参数重用表现出更短的计算时间。