Latency-Aware Client Selection and Energy Management for Hierarchical Federated Learning

With the prosperity of deep learning (DL) in Internet of Things (IoT) fields, federated learning (FL), viewed as a critical element of numerous DL-aided IoT intelligent applications, enables cooperative DL training across decentralized clients without revealing their personal data. However, the computational capacity heterogeneity and limited energy resources of IoT devices cause a huge negative influence on FL training in IoT intelligence applications. To address the above issues, this paper proposes an excellent distributed training mechanism for hierarchical FL to reduce training latency and energy cost for achieving the desirable accuracy. Specifically, taking into account computational capacity heterogeneity of clients, we first design a latency-regularization-aware client selection algorithm to appropriately select participating clients in training epochs and control their participation frequencies for boosting distributed training efficiency. Subsequently, after obtaining the selected client subset in each hierarchical FL training epoch, by leveraging the variable transmission delays of clients in distributed training, we propose a mixed integer linear programming-based transmission power management strategy for participating clients to alleviate their energy consumption burden. Extensive numerical results demonstrate that our proposed mechanism can attain 576.93% training speedup and achieve 10.94% accuracy enhancement compared with the baseline General FL, and yield up to 56.91% energy cost savings compared with the baseline HierFAVG.