通信学报 ›› 2021, Vol. 42 ›› Issue (6): 1-29.doi: 10.11959/j.issn.1000-436x.2021056

所属专题: 联邦学习

• 学术论文 •    下一篇

联邦学习中基于时分多址接入的用户调度策略

陶梅霞, 王栋, 孙瑞, 张乃夫   

  1. 上海交通大学电子信息与电气工程学院,上海 200240
  • 修回日期:2020-11-13 出版日期:2021-06-25 发布日期:2021-06-01
  • 作者简介:陶梅霞(1978− ),女,江西九江人,博士,上海交通大学教授,主要研究方向为无线缓存、边缘计算、资源分配等
    王栋(1992− ),男,陕西渭南人,上海交通大学博士生,主要研究方向为联邦学习、边缘计算和无线通信网络等
    孙瑞(1996− ),女,江苏盐城人,上海交通大学硕士生,主要研究方向为联邦学习、移动边缘计算、分布式计算等
    张乃夫(1993− ),男,黑龙江哈尔滨人,上海交通大学博士生,主要研究方向为边缘学习、联邦学习等
  • 基金资助:
    国家重点研发计划基金资助项目(2019YFB1802702);国家自然科学基金资助项目(61941106)

TDMA-based user scheduling policies for federated learning

Meixia TAO, Dong WANG, Rui SUN, Naifu ZHANG   

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • Revised:2020-11-13 Online:2021-06-25 Published:2021-06-01
  • Supported by:
    The National Key Research and Development Program of China(2019YFB1802702);The National Natural Science Foundation of China(61941106)

摘要:

为了提高联邦学习的通信效率,针对用户计算能力和信道状态异构的场景,提出了一类基于时分多址接入的用户调度策略,在满足给定单轮模型训练所需计算的样本数量约束下,最小化单轮模型更新的系统时延。理论分析了该调度策略的预期收敛速度,探究收敛性能与系统总时延的均衡关系,并进一步分析最优批大小的选择问题。仿真结果显示,所提算法与基准算法相比,模型收敛速率提升30%以上。

关键词: 联邦学习, 背包问题, 用户调度, 收敛性分析, 边缘智能

Abstract:

To improve the communication efficiency in FL (federated learning), for the scenario with heterogeneous edge user's computing capacity and channel state, a class of time division multiple access (TDMA) based user scheduling policies were proposed for FL.The proposed policies aim to minimize the system delay in each round of model training subject to a given sample size constraint required for computing in each round.In addition, the convergence rate of the proposed scheduling algorithms was analyzed from a theoretical perspective to study the tradeoff between the convergence performance and the total system delay.The selection of the optimal batch size was further analyzed.Simulation results show that the convergence rate of the proposed algorithm is at least 30% higher than all the considered benchmarks.

Key words: federated learning, knapsack problem, user scheduling, convergence analysis, edge intelligence

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