A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning  

作　　者：Zerui Zhen Zihao Wu Lei Feng Wenjing Li Feng Qi Shixuan Guo 

机构地区：[1]Beijing University of Posts and Telecommunication,Beijing,100876,China [2]Vanderbilt University,Nashville TN,37240,USA

出　　处：《Computers, Materials & Continua》2023年第5期2939-2955,共17页计算机、材料和连续体（英文）

基　　金：This work was funded by National Key R&D Program of China(Grant No.2020YFB0906003).

摘　　要：Asynchronous federated learning(AsynFL)can effectivelymitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security.However,the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning(FL)algorithm uses synchronous or asynchronous aggregation.Therefore,there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction.This paper proposes a novel Fixed-point Asynchronous Federated Learning(FixedAsynFL)algorithm,which could mitigate the resource consumption caused by frequent data communication while alleviating the effect of device heterogeneity.FixedAsynFL uses fixed-point quantization to compress the local and global models in AsynFL.In order to balance energy consumption and learning accuracy,this paper proposed a quantization scale selection mechanism.This paper examines the mathematical relationship between the quantization scale and energy consumption of the computation/communication process in the FixedAsynFL.Based on considering the upper bound of quantization noise,this paper optimizes the quantization scale by minimizing communication and computation consumption.This paper performs pertinent experiments on the MNIST dataset with several edge nodes of different computing efficiency.The results show that the FixedAsynFL algorithm with an 8-bit quantization can significantly reduce the communication data size by 81.3%and save the computation energy in the training phase by 74.9%without significant loss of accuracy.According to the experimental results,we can see that the proposed AsynFixedFL algorithm can effectively solve the problem of device heterogeneity and energy consumption limitation of edge nodes.

关 键 词：Asynchronous federated learning artificial intelligence model compression energy consumption fixed-point quantization learning accuracy 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]