Federated learning (FL) is a distributed and privacy-preserving learning framework for predictive modeling with massive data generated at the edge by Internet of Things (IoT) devices. One major challenge preventing the wide adoption of FL in IoT is the pervasive power supply constraints of IoT devices due to the intensive energy consumption of battery-powered clients for local training and model updates. Low battery levels of clients eventually lead to their early dropouts from edge networks, loss of training data jeopardizing the performance of FL, and their availability to perform other designated tasks. In this paper, we propose FedLE, an energy-efficient client selection framework that enables lifespan extension of edge IoT networks. In FedLE, the clients first run for a minimum epoch to generate their local model update. The models are partially uploaded to the server for calculating similarities between each pair of clients. Clustering is performed against these client pairs to identify those with similar model distributions. In each round, low-powered clients have a lower probability of being selected, delaying the draining of their batteries. Empirical studies show that FedLE outperforms baselines on benchmark datasets and lasts more training rounds than FedAvg with battery power constraints.

翻译：联邦学习是一种分布式且保护隐私的学习框架，适用于利用物联网边缘设备生成的海量数据进行预测建模。阻碍联邦学习在物联网中广泛应用的主要挑战之一，在于电池供电的客户端进行本地训练和模型更新时能耗巨大，导致物联网设备普遍面临供电限制。客户端电池电量不足最终会使其过早退出边缘网络，这不仅导致训练数据丢失而损害联邦学习性能，还会影响其执行其他指定任务的能力。本文提出FedLE——一种能实现边缘物联网网络生命周期扩展的能效型客户端选择框架。在FedLE中，客户端首先运行最小轮数生成本地模型更新，随后将部分模型上传至服务器以计算客户端间的相似度。通过对客户端对进行聚类，识别具有相似模型分布的客户端组。在每轮训练中，低电量客户端被选中的概率较低，从而延缓其电池耗尽。实验表明，FedLE在基准数据集上优于基线方法，且在电池供电限制下比FedAvg能够维持更多训练轮次。