摘要
在分层联邦学习中,能量受限的移动设备参与模型训练会消耗自身资源。为了降低移动设备的能耗,文中在不超过分层联邦学习的最大容忍时间下,提出了移动设备能耗之和最小化问题。不同训练轮次的边缘服务器能够选择不同的移动设备,移动设备也能够为不同的边缘服务器并发训练模型,因此文中基于在线双边拍卖机制提出了ODAM-DS算法。基于最优停止理论,支持边缘服务器在合适的时刻选择移动设备,使得移动设备的平均能耗最小,然后对提出的在线双边拍卖机制进行理论分析,证明其满足激励相容性、个体理性、弱预算均衡约束等特性。模拟实验的结果证明,ODAM-DS算法产生的能耗比已有的HFEL算法平均降低了19.04%。
In hierarchical federated learning,energy constrained mobile devices will consume their own resources for participating in model training.In order to reduce the energy consumption of mobile devices,this paper proposes the problem of minimizing the sum of energy consumption of mobile devices without exceeding the maximum tolerance time of hierarchical federated learning.Different training rounds of edge server can select different mobile devices,and mobile devices can also train models under different edge servers concurrently.Therefore,this paper proposes ODAM-DS algorithm based on an online double auction mechanism.Based on the optimal stopping theory,the edge server is supported to select the mobile device at the best time,so as to minimize the average energy consumption of the mobile device.Then,the theoretical analysis of the proposed online double auction mechanism proves that it meets the characteristics of incentive compatibility,individual rationality and weak budget equilibrium constraints.Simulation results show that the energy consumption of ODAM-DS algorithm is 19.04%lower than that of the existing HFEL algorithm.
作者
杜辉
李卓
陈昕
DU Hui;LI Zhuo;CHEN Xin(Beijing Key Laboratory of Internet Culture and Digital Dissemination Research(Beijing Information Science&Technology University),Beijing 100101,China;School of Computer Science,Beijing Information Science&Technology University,Beijing 100101,China)
出处
《计算机科学》
CSCD
北大核心
2022年第3期23-30,共8页
Computer Science
基金
国家自然科学基金(61872044)
北京市青年拔尖人才项目
网络与文化传播北京市重点实验室开放课题。
关键词
分层联邦学习
能耗最小化
在线双边拍卖
最优停止理论
激励机制设计
Hierarchical federated learning
Minimization of energy consumption
Online double auction
Optimal stopping theory
Incentive mechanism design
