  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.
