Federated learning (FL) achieves great popularity in broad areas as a
powerful interface to offer intelligent services to customers while maintaining
data privacy. Nevertheless, FL faces communication and computation bottlenecks
due to limited bandwidth and resource constraints of edge devices. To
comprehensively address the bottlenecks, the technique of dropout is
introduced, where resource-constrained edge devices are allowed to
collaboratively train a subset of the global model parameters. However, dropout
impedes the learning efficiency of FL under unbalanced local data
distributions. As a result, FL requires more rounds to achieve appropriate
accuracy, consuming more communication and computation resources. In this
paper, we present FLrce, an efficient FL framework with a relationship-based
client selection and early-stopping strategy. FLrce accelerates the FL process
by selecting clients with more significant effects, enabling the global model
to converge to a high accuracy in fewer rounds. FLrce also leverages an early
stopping mechanism to terminate FL in advance to save communication and
computation resources. Experiment results show that FLrce increases the
communication and computation efficiency by 6% to 73.9% and 20% to 79.5%,
respectively, while maintaining competitive accuracy.

FLrce 是一种高效的联邦学习框架，具有基于关系的客户选择和提前停止策略，可以加速 FL 过程并节省通信和计算资源。实验结果表明，FLrce 提高了通信和计算效率，并保持了竞争性的准确性。