Energy or Accuracy? Near-Optimal User Selection and Aggregator Placement for Federated Learning in MEC

To unveil the hidden value in the datasets of user equipments (UEs) while preserving user privacy, federated learning (FL) is emerging as a promising technique to train a machine learning model using the datasets of UEs locally without uploading the datasets to a central location. Customers require...

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Veröffentlicht in:IEEE transactions on mobile computing Jg. 23; H. 3; S. 2470 - 2485
Hauptverfasser: Xu, Zichuan, Li, Dongrui, Liang, Weifa, Xu, Wenzheng, Xia, Qiufen, Zhou, Pan, Rana, Omer F., Li, Hao
Format: Magazine Article
Sprache:Englisch
Veröffentlicht: Los Alamitos IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Algorithms
Approximation
Approximation algorithms
Base stations
Computational modeling
Datasets
Decision theory
Distance learning
Edge computing
Energy consumption
Energy minimization
Federated learning
Machine learning
machine learning based algorithms
Mathematical analysis
Minimization
Mobile computing
mobile edge computing
Optimization
Placement
Training
UE selection and aggregator placement
ISSN:1536-1233, 1558-0660
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Zusammenfassung:To unveil the hidden value in the datasets of user equipments (UEs) while preserving user privacy, federated learning (FL) is emerging as a promising technique to train a machine learning model using the datasets of UEs locally without uploading the datasets to a central location. Customers require to train machine learning models based on different datasets of UEs, through issuing FL requests that are implemented by FL services in a mobile edge computing (MEC) network. A key challenge of enabling FL in MEC networks is how to minimize the energy consumption of implementing FL requests while guaranteeing the accuracy of machine learning models, given that the availabilities of UEs usually are uncertain. In this paper, we investigate the problem of energy minimization for FL in an MEC network with uncertain availabilities of UEs. We first consider the energy minimization problem for a single FL request in an MEC network. We then propose a novel optimization framework for the problem with a single FL request, which consists of (1) an online learning algorithm with a bounded regret for the UE selection, by considering various contexts (side information) that influence energy consumption; and (2) an approximation algorithm with an approximation ratio for the aggregator placement for a single FL request. We third deal with the problem with multiple FL requests, for which we devise an online learning algorithm with a bounded regret. We finally evaluate the performance of the proposed algorithms by extensive experiments. Experimental results show that the proposed algorithms outperform their counterparts by reducing at least 13% of the total energy consumption while achieving the same accuracy.
Bibliographie:ObjectType-Article-1
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ISSN:1536-1233
1558-0660
DOI:10.1109/TMC.2023.3262829