ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (12): 2571-2582.doi: 10.7544/issn1000-1239.2020.20190754

• 人工智能 • 上一篇    下一篇


芦效峰1,廖钰盈1,Pietro Lio2,Pan Hui3   

  1. 1(北京邮电大学网络空间安全学院 北京 100876);2(剑桥大学计算机实验室 英国剑桥 CB3 0FD);3(香港科技大学计算机科学与工程学院 香港 999077) (
  • 出版日期: 2020-12-01
  • 基金资助: 

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.

摘要: 随着物联网和移动设备性能的不断提高,一种新型计算架构——边缘计算——应运而生.边缘计算的出现改变了数据需要集中上传到云端进行处理的局面,最大化利用边缘物联网设备的计算和存储能力.边缘计算节点对本地数据进行处理,不再需要把大量的本地数据上传到云端进行处理,减少了数据传输的延时.在边缘网络设备上进行人工智能运算的需求也在逐日增大,因为联邦学习机制不需要把数据集中后进行模型训练,所以更适合于节点平均数据量有限的边缘网络机器学习的场景.针对以上挑战,提出了一种面向边缘网络计算的高效异步联邦学习机制(efficient asynchronous federated learning mechanism for edge network computing, EAFLM),根据自适应的阈值对训练过程中节点与参数服务器之间的冗余通信进行压缩.其中,双重权重修正的梯度更新算法,允许节点在学习的任何过程中加入或退出联邦学习.实验显示提出的方法将梯度通信压缩至原通信次数的8.77%时,准确率仅降低0.03%.

关键词: 联邦学习, 边缘计算, 异步分布式学习, 梯度压缩, 隐私保护

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