HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing

被引：19

作者：

Xu, Zichuan ^{[1
]}

Zhao, Dapeng ^{[1
]}

Liang, Weifa ^{[2
]}

Rana, Omer F. ^{[3
]}

Zhou, Pan ^{[4
]}

Li, Mingchu ^{[1
]}

Xu, Wenzheng ^{[5
]}

Li, Hao ^{[6
]}

Xia, Qiufen ^{[7
]}

机构：

[1] Dalian Univ Technol, Sch Software, Key Lab Ubiquitous Network & Serv Software Liaonin, Dalian 116621, Liaoning, Peoples R China

[2] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China

[3] Cardiff Univ, Phys Sci & Engn Coll, Cardiff CF10 3AT, Wales

[4] Huazhong Univ Sci & Technol, Hubei Engn Res Ctr Big Data Secur, Sch Cyber Sci & Engn, Hubei Key Lab Distributed Syst Secur, Wuhan 430074, Hubei, Peoples R China

[5] Sichuan Univ, Dept Comp Sci, Chengdu 610065, Sichuan, Peoples R China

[6] Ningxia Res Inst, China Coal Res Inst, Beijing 100013, Peoples R China

[7] Dalian Univ Technol, Int Sch Informat Sci & Engn, Key Lab Ubiquitous Network & Serv Software Liaonin, Dalian 116621, Liaoning, Peoples R China

来源：

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS | 2023年 / 34卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Mobile edge computing; federated learning; aggregator placement and UE assignment; hierarchical federated learning framework; approximation algorithms; online learning algorithms; RESOURCE-ALLOCATION; OPTIMIZATION; FRAMEWORK; NETWORKS;

D O I：

10.1109/TPDS.2022.3218807

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Federated learning (FL) is a distributed machine learning technique that enables model development on user equipments (UEs) locally, without violating their data privacy requirements. Conventional FL adopts a single parameter server to aggregate local models from UEs, and can suffer from efficiency and reliability issues - especially when multiple users issue concurrent FL requests. Hierarchical FL consisting of a master aggregator and multiple worker aggregators to collectively combine trained local models from UEs is emerging as a solution to efficient and reliable FL. The placement of worker aggregators and assignment of UEs to worker aggregators plays a vital role in minimizing the cost of implementing FL requests in a Mobile Edge Computing (MEC) network. Cost minimization associated with joint worker aggregator placement and UE assignment problem in an MEC network is investigated in this work. An optimization framework for FL and an approximation algorithm with an approximation ratio for a single FL request is proposed. Online worker aggregator placements and UE assignments for dynamic FL request admissions with uncertain neural network models, where FL requests arrive one by one without the knowledge of future arrivals, is also investigated by proposing an online learning algorithm with a bounded regret. The performance of the proposed algorithms is evaluated using both simulations and experiments in a real testbed with its hardware consisting of server edge servers and devices and software built upon an open source hierarchical FedML (HierFedML) environment. Simulation results show that the performance of the proposed algorithms outperform their benchmark counterparts, by reducing the implementation cost by at least 15% per FL request. Experimental results in the testbed demonstrate the performance gain using the proposed algorithms using real datasets for image identification and text recognition applications.

引用

页码：328 / 345

页数：18