跨设备联邦学习中的客户端选择算法  

Client Selection Algorithm in Cross-device Federated Learning

作　　者：张瑞麟 杜晋华 尹浩[2] ZHANG Rui-Lin;DU Jin-Hua;YIN Hao(Department of Computer Science and Technology,Tsinghua University,Beijing100084,China;Beijing National Research Center for Information Science and Technology,Tsinghua University,Beijing100084,China)

机构地区：[1]清华大学计算机科学与技术系,北京100084 [2]清华大学北京信息科学与技术国家研究中心,北京100084

出　　处：《软件学报》2024年第12期5725-5740,共16页Journal of Software

基　　金：国家重点研发计划(2022YFB2702801);国家自然科学基金(92067206,61972222,62102217)。

摘　　要：联邦学习是一种新型的分布式机器学习范式,它在满足用户隐私和数据保密性要求的前提下,充分利用众多分散客户端的计算能力及其本地数据联合训练机器学习模型.在跨设备联邦学习场景下,客户端通常由数千甚至万级别的移动设备或端侧设备组成,由于通信和计算成本的限制,聚合服务器在每个训练轮次中仅选择少量客户端加入训练.几种被广泛应用的联邦优化算法均采用完全随机的客户端选择算法,但这被证明有着很大的优化空间.近年来,如何高效可靠地从海量异构客户端中选择合适的集合参与训练,以优化联邦学习协议的资源消耗和模型性能被广泛研究,但仍没有文献对这一关键问题进行综合调研.需要对跨设备联邦学习的客户端选择算法研究进行全面调研.具体地,形式化描述客户端选择问题,然后给出对选择算法的分类并逐一深入讨论分析.最后,讨论客户端选择算法的一些未来研究方向.As a new type of distributed machine learning paradigm,federated learning makes full use of the computing power of many distributed clients and their local data to jointly train a machine learning model under the premise of meeting user privacy and data confidentiality requirements.In cross-device federated learning scenarios,the client usually consists of thousands or even tens of thousands of mobile devices or terminal devices.Due to the limitations of communication and computing costs,the aggregation server only selects few clients for the training during each round of training.Meanwhile,several widely employed federated optimization algorithms adopt a completely random client selection algorithm,which has been proven to have a huge optimization space.In recent years,how to efficiently and reliably select a suitable set from massive heterogeneous clients to participate in training and thus optimize the resource consumption and model performance of federated learning protocols has been extensively studied,but there is still no comprehensive investigation on the key issue.Therefore,this study conducts a comprehensive survey of client selection algorithms for cross-device federated learning.Specifically,it provides a formal description of the client selection problem,then gives the classification of selection algorithms,and discusses and analyzes the algorithms one by one.Finally,some future research directions for client selection algorithms are explored.

关 键 词：跨设备联邦学习 客户端选择 组合优化 效用理论 

分 类 号：TP303[自动化与计算机技术—计算机系统结构]