FedSA: A Semi-Asynchronous Federated Learning Mechanism in Heterogeneous Edge Computing

计算机科学 异步通信 GSM演进的增强数据速率 趋同(经济学) 资源消耗 人工智能 约束(计算机辅助设计) 上下界 机制(生物学) 机器学习 分布式计算 理论计算机科学 计算机网络 数学 认识论 几何学 数学分析 哲学 生物 经济增长 经济 生态学
作者
Qianpiao Ma,Yang Xu,Hongli Xu,Zhida Jiang,Liusheng Huang,He Huang
出处
期刊:IEEE Journal on Selected Areas in Communications [Institute of Electrical and Electronics Engineers]
卷期号:39 (12): 3654-3672 被引量:75
标识
DOI:10.1109/jsac.2021.3118435
摘要

Federated learning (FL) involves training machine learning models over distributed edge nodes ( i.e. , workers) while facing three critical challenges, edge heterogeneity, Non-IID data and communication resource constraint. In the synchronous FL, the parameter server has to wait for the slowest workers, leading to significant waiting time due to edge heterogeneity. Though asynchronous FL can well tackle the edge heterogeneity, it requires frequent model transfers, resulting in massive communication resource consumption. Moreover, the different relative frequency of workers participating in asynchronous updating may seriously hurt training accuracy, especially on Non-IID data. In this paper, we propose a semi-asynchronous federated learning mechanism (FedSA), where the parameter server aggregates a certain number of local models by their arrival order in each round. We theoretically analyze the quantitative relationship between the convergence bound of FedSA and different factors, e.g. , the number of participating workers in each round, the degree of data Non-IID and edge heterogeneity. Based on the convergence bound, we present an efficient algorithm to determine the number of participating workers to minimize the training completion time. To further improve the training accuracy on Non-IID data, FedSA deploys adaptive learning rates for workers by their relative participation frequency. We extend our proposed mechanism to the dynamic and multiple learning tasks scenarios. Experimental results on the testbed show that our proposed mechanism and algorithms address the three challenges more effectively than the state-of-the-art solutions.
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