Journal
IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume 22, Issue 7, Pages 3805-3822Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2022.3147792
Keywords
Hierarchical federated learning; mobile edge computing; cluster construction; optimization
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This paper proposes an efficient federated learning mechanism called FedCH to accelerate federated learning in heterogeneous edge computing. By constructing a special cluster topology and performing hierarchical aggregation, FedCH reduces completion time and network traffic compared to existing mechanisms.
Federated learning (FL) has emerged in edge computing to address the limited bandwidth and privacy concerns of traditional cloud-based training. However, the existing FL mechanisms may lead to a long training time and consume massive communication resources. In this paper, we propose an efficient FL mechanism, namely FedCH, to accelerate FL in heterogeneous edge computing. Different from existing works which adopt the pre-defined system architecture and train models in a synchronous or asynchronous manner, FedCH will construct a special cluster topology and perform hierarchical aggregation for training. Specifically, FedCH arranges all clients into multiple clusters based on their heterogeneous training capacities. The clients in one cluster synchronously forward their local updates to the cluster header for aggregation, while all cluster headers take the asynchronous method for global aggregation. Our analysis shows that the convergence bound depends on the number of clusters and the training epochs. We propose efficient algorithms to determine the optimal number of clusters with resource budgets and then construct the cluster topology to address the client heterogeneity. Extensive experiments on both physical platform and simulated environment show that FedCH reduces the completion time by 49.5-79.5% and the network traffic by 57.4-80.8%, compared with the existing FL mechanisms.
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