ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (12): 2571-2582.doi: 10.7544/issn1000-1239.2020.20190754

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An Asynchronous Federated Learning Mechanism for Edge Network Computing

Lu Xiaofeng1, Liao Yuying1, Pietro Lio2, Pan Hui3   

  1. 1(School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876);2(Computer Laboratory, University of Cambridge, Cambridge CB3 0FD);3(Department of Computer Science & Engineering, Hong Kong University of Science and Technology, Hong Kong 999077)
  • Online:2020-12-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61472046), the Beijing Association for Science and Technology Seed Fund, and the Ant Financial Security Special Research Fund.

Abstract: With the continuous improvement of the performance of the IoT and mobile devices, a new type of computing architecture, edge computing, came into being. The emergence of edge computing has changed the situation where data needs to be uploaded to the cloud for data processing, fully utilizing the computing and storage capabilities of edge IoT devices. Edge nodes process private data locally and no longer need upload a large amount of data to the cloud for processing, reducing the transmission delay. The demand for implementing artificial intelligence frameworks on edge nodes is also increasing day by day. Because the federated learning mechanism does not require centralized data for model training, it is more suitable for edge network machine learning scenarios where the average amount of data of nodes is limited. This paper proposes an efficient asynchronous federated learning mechanism for edge network computing (EAFLM), which compresses the redundant communication between the nodes and the parameter server during the training process according to the self-adaptive threshold. The gradient update algorithm based on dual-weight correction allows nodes to join or withdraw from federated learning during any process of learning. Experimental results show that when the gradient communication is compressed to 8.77% of the original communication times, the accuracy of the test set is only reduced by 0.03%.

Key words: federated learning, edge computing, asynchronous distributed learning, gradient compression, privacy-preserving

CLC Number: