Energy-efficient personalized Federated Learning for establishing Green IoT

Abstract

Green Internet of Things (Green IoT) is a technique that intends to reduce energy consumption and carbon emissions of Internet of Things (IoT) devices by optimizing hardware design, communication protocols, and data processing. One of the most promising schemes to realize Green IoT is personalized Federated Learning (pFL). Unfortunately, existing pFL methods still need further improvement in achieving Green IoT from the following aspects. 1) Computational energy consumption: model training on IoT devices generates a substantial amount of computational energy consumption. 2) Model performance: the dynamic role differences in each layer of the trained deep neural network need to be considered. Jointly considering these aspects, we present a novel pFL framework named Energy-Efficient personalized Federated Learning (EE-pFL) for establishing Green IoT. Specifically, an IoT device serves as an edge server. Each IoT device produces a customized model through a model training phase and a model aggregation phase. In the model training phase, a threshold-based sparsification strategy is introduced to reduce the computational energy consumption of IoT devices by selectively executing parameter updates. In the model aggregation phase, layer aggregation and an Adaptive Weight Calculation (AWC) mechanism are proposed to capture dynamic role differences in different layers of a deep neural network. Experimental results demonstrate that EEpFL shows lower computational energy consumption and higher classification accuracy than advanced benchmarks.