Efficient Adaptive Federated Learning in Resource-Constrained IoT Environments
Zunming Chen, Hongyan Cui, Qiuji Luan, Xi Yu · 2023
Federated Learning (FL) has emerged as a privacy-preserving distributed learning framework which enables IoT devices to collaboratively train machine learning models vi-a sharing model parameters. However, inefficiency due to frequent parameters transmissions significantly reduces FL performance. Existing acceleration algorithms for speeding up FL training consist of two main types including local update and parameter compression which consider the trade-offs between communication and computation/precision respectively. Jointly considering these two trade-offs and adaptively balancing their impacts on convergence have remained unresolved. To solve the problem, we propose an efficient adaptive federated optimization (EAFO) algorithm to improve the efficiency of FL in resource-constrained IoT environments, which minimizes the learning error by the joint consideration of two variables consisting of the local update and parameter compression. The EAFO enables FL to adaptively adjust two variables and balance trade-offs among computation, communication, and precision. The experiment results illustrate the high effective-ness of the proposed EAFO algorithm, which can achieve higher accuracies faster compared with the state-of-the-art algorithms.