Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression
发布时间：2026-03-05
点击次数：
发表刊物：IEEE Transactions on Mobile Computing (CCF-A)
关键字：Decentralized optimization, federated learning, gradient compression, heterogeneity, local updating, mobile edge networks.
摘要：Motivated by the drawbacks of cloud-based federated learning (FL), cooperative federated edge learning (CFEL) has been proposed to improve efficiency for FL over mobile edge networks, where multiple edge servers collaboratively coordinate the distributed model training across a large number of edge devices. However, CFEL faces critical challenges arising from dynamic and heterogeneous device properties, which slow down the convergence and increase resource consumption. This paper proposes a heterogeneity-aware CFEL scheme called Heterogeneity-Aware Cooperative Edge-based Federated Averaging (HCEF) that aims to maximize the model accuracy while minimizing the training time and energy consumption via adaptive computation and communication compression in CFEL. By theoretically analyzing how local update frequency and gradient compression affect the convergence error bound in CFEL, we develop an efficient online control algorithm for HCEF to dynamically determine local update frequencies and compression ratios for heterogeneous devices. Experimental results show that compared with prior schemes, the proposed HCEF scheme can maintain higher model accuracy while reducing training latency and improving energy efficiency simultaneously.
合写作者：Zhidong Gao, Yuanxiong Guo, Yanmin Gong
第一作者：Zhenxiao Zhang
论文类型：期刊论文
是否译文：否
发表时间：2024-11-06