Federated learning is an emerging distributed machine learning framework in the Internet of Vehicles (IoV). In IoV, millions of vehicles are willing to train the model to share their knowledge. Maintaining an active state means the participants must update their state to the FL server in a fixed interval and participate to next round. However, the cost by maintaining an active state is very large when there are a huge number of participating vehicles. In this paper, we proposed a distributed client selection scheme to reduce the cost of maintaining the active state for all participants. The clients with the highest evaluation are elected among the neighbours. In the evaluator, four variables are considered including sample quantity, throughput available, computational capability and the quality of the local dataset. We adopted fuzzy logic as the evaluator since the closed-form solution over four variables does not exist. Extensive simulation results show our proposal approximates the centralized client selection in terms of accuracy and can significantly reduce the communication overhead.

翻译：联邦学习是车联网中一种新兴的分布式机器学习框架。在车联网中，数百万车辆愿意参与模型训练以共享自身知识。维持活跃状态要求参与者以固定间隔向联邦学习服务器更新状态并参与下一轮训练。然而，当参与车辆数量巨大时，维持所有参与者活跃状态的成本极高。本文提出了一种分布式客户端选择方案以降低维持所有参与者活跃状态的成本。该方案通过邻居节点间的选举机制，选择评估得分最高的客户端。在评估器中综合考虑四个变量：样本数量、可用吞吐量、计算能力以及本地数据集质量。由于四个变量不存在闭式解，我们采用模糊逻辑作为评估器。大量仿真结果表明，该方案在精度上接近集中式客户端选择，且能显著降低通信开销。