Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data

• URL: http://arxiv.org/abs/2104.12678v2
• Date: Tue, 27 Apr 2021 04:27:54 GMT
• Title: Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data
• Authors: Yuchang Sun and Jiawei Shao and Yuyi Mao and Jun Zhang
• Abstract summary: Federated edge learning (FEEL) has emerged as an effective alternative to reduce the large communication latency in Cloud-based machine learning solutions. We investigate a novel framework of FEEL, namely semi-decentralized federated edge learning (SD-FEEL) By allowing model aggregation between different edge clusters, SD-FEEL enjoys the benefit of FEEL in reducing training latency and improves the learning performance.
• Score: 3.983055670167878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated edge learning (FEEL) has emerged as an effective alternative to reduce the large communication latency in Cloud-based machine learning solutions, while preserving data privacy. Unfortunately, the learning performance of FEEL may be compromised due to limited training data in a single edge cluster. In this paper, we investigate a novel framework of FEEL, namely semi-decentralized federated edge learning (SD-FEEL). By allowing model aggregation between different edge clusters, SD-FEEL enjoys the benefit of FEEL in reducing training latency and improves the learning performance by accessing richer training data from multiple edge clusters. A training algorithm for SD-FEEL with three main procedures in each round is presented, including local model updates, intra-cluster and inter-cluster model aggregations, and it is proved to converge on non-independent and identically distributed (non-IID) data. We also characterize the interplay between the network topology of the edge servers and the communication overhead of inter-cluster model aggregation on training performance. Experiment results corroborate our analysis and demonstrate the effectiveness of SD-FFEL in achieving fast convergence. Besides, guidelines on choosing critical hyper-parameters of the training algorithm are also provided.

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