FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing

Abstract

Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without uploading the local data. Gradient compression may be applied to FL to alleviate the communication overheads but current FL with gradient compression still faces great challenges. To deploy green MEC, we propose FedGreen, which enhances the original FL with fine-grained gradient compression to efficiently control the total energy consumption of the devices. Specifically, we introduce the relevant operations including device-side gradient reduction and server-side element-wise aggregation to facilitate the gradient compression in FL. According to a public dataset, we investigate the contributions of the compressed local gradients with respect to different compression ratios. After that, we formulate and tackle a learning accuracy-energy efficiency tradeoff problem where the optimal compression ratio and computing frequency are derived for each device. Experiments results demonstrate that given the 80% test accuracy requirement, compared with the baseline schemes, FedGreen reduces at least 32% of the total energy consumption of the devices.

arXiv commentAccepted for publication in Globecom'21

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Cited by in corpus (2)

• AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices
• FAST: Fidelity-Adjustable Semantic Transmission over Heterogeneous Wireless Networks