A game incentive mechanism for energy efficient federated learning in computing power networks  

作　　者：Xiao Lin Ruolin Wu Haibo Mei Kun Yang 

机构地区：[1]School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu,China [2]Chengdu Tongfei Technology,Co.,Ltd,Chengdu,China

出　　处：《Digital Communications and Networks》2024年第6期1741-1747,共7页数字通信与网络(英文版)

基　　金：partly funded by MOST Major Research and Development Project(Grant No 2021YFB2900204);Natural Science Foundation of China(Grant No 62132004);Sichuan Major R&D Project(Grant No 22QYCX0168);the Key Research and Development Program of Zhejiang Province(Grant No 2022C01093)。

摘　　要：Computing Power Network(CPN)is emerging as one of the important research interests in beyond 5G(B5G)or 6G.This paper constructs a CPN based on Federated Learning(FL),where all Multi-access Edge Computing(MEC)servers are linked to a computing power center via wireless links.Through this FL procedure,each MEC server in CPN can independently train the learning models using localized data,thus preserving data privacy.However,it is challenging to motivate MEC servers to participate in the FL process in an efficient way and difficult to ensure energy efficiency for MEC servers.To address these issues,we first introduce an incentive mechanism using the Stackelberg game framework to motivate MEC servers.Afterwards,we formulate a comprehensive algorithm to jointly optimize the communication resource(wireless bandwidth and transmission power)allocations and the computation resource(computation capacity of MEC servers)allocations while ensuring the local accuracy of the training of each MEC server.The numerical data validates that the proposed incentive mechanism and joint optimization algorithm do improve the energy efficiency and performance of the considered CPN.

关 键 词：Computing power network Federated learning Energy efficiency Stackelberg game Resource allocation 

分 类 号：TN929.5[电子电信—通信与信息系统] O225[电子电信—信息与通信工程]