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Federated learning with adaptive communication compression under dynamic bandwidth and unreliable networks
Information Sciences ( IF 8.1 ) Pub Date : 2020-06-18 , DOI: 10.1016/j.ins.2020.05.137
Xiongtao Zhang , Xiaomin Zhu , Ji Wang , Hui Yan , Huangke Chen , Weidong Bao

Emerging issues such as privacy protection and communication limitations make it not possible to collect all data into data centers, which has driven the paradigm of big data and artificial intelligence to sink to network edge. Because of having the ability to continuously learn newly generated data from the Internet of Things and mobile devices while protecting user privacy, federated learning has been recognized as a new parallel distributed technology for big data and artificial intelligence. However, traditional federated learning is too strict on network throughput and is susceptible to unreliable networks and dynamic bandwidth. To address these communication bottlenecks in federated learning, this study proposes a cloud-edge-clients federated learning architecture Cecilia and designs a new algorithm ACFL. ACFL employs an information sharing method different from the traditional federated learning, and can adaptively compress shared information according to network conditions. The convergence of ACFL is analyzed from a theoretical perspective. In addition, the performance of the ACFL is evaluated through typical machine learning tasks with real datasets, including image classification, sentiment analysis, and next character prediction. Both theoretical and experimental results show that Cecilia and ACFL can better adapt to dynamic bandwidth and unreliable networks when performing federated learning.



中文翻译:

动态带宽和不可靠网络下的自适应通信压缩联合学习

诸如隐私保护和通信限制之类的新兴问题使得不可能将所有数据收集到数据中心,这驱使大数据和人工智能的范式陷入网络边缘。由于能够在保护用户隐私的同时不断地从物联网和移动设备中学习新生成的数据,因此联合学习已被公认为是一种用于大数据和人工智能的新型并行分布式技术。但是,传统的联合学习对网络吞吐量过于严格,容易受到不可靠的网络和动态带宽的影响。为了解决联合学习中的这些通信瓶颈,本研究提出了云边缘客户端联合学习架构Cecilia,并设计了一种新算法ACFL。ACFL采用不同于传统联合学习的信息共享方法,并且可以根据网络条件自适应地压缩共享信息。从理论角度分析了ACFL的收敛性。此外,ACFL的性能通过具有真实数据集的典型机器学习任务进行评估,包括图像分类,情感分析和下一字符预测。理论和实验结果均表明,当进行联合学习时,塞西莉亚和ACFL可以更好地适应动态带宽和不可靠的网络。ACFL的性能通过具有真实数据集的典型机器学习任务进行评估,包括图像分类,情感分析和下一字符预测。理论和实验结果均表明,当进行联合学习时,塞西莉亚和ACFL可以更好地适应动态带宽和不可靠的网络。ACFL的性能通过具有真实数据集的典型机器学习任务进行评估,包括图像分类,情感分析和下一字符预测。理论和实验结果均表明,当进行联合学习时,塞西莉亚和ACFL可以更好地适应动态带宽和不可靠的网络。

更新日期:2020-06-18
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