Energy-Efficient Mutual Learning over D2D Communications

Mutual learning (ML) is a promising technique for sharing knowledge in data while keeping the data privacy and preserving the individual characteristics of the local model. In this paper, we design a novel decentralized mutual learning (DML) system, where bidirectional device-to-device (D2D) communi...

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Vydané v:IEEE Transactions on Vehicular Technology Ročník 72; číslo 12; s. 1 - 13
Hlavní autori: Yin, Rui, Lu, Xiao, Chen, Chao, Chen, Xianfu, Wu, Celimuge
Médium: Journal Article
Jazyk:English
Japanese
Vydavateľské údaje: New York IEEE 01.12.2023
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
asynchronous nature
Convexity
D2D communications
Data models
decentralized system
Device-to-device communication
Electronic devices
Energy consumption
energy saving
Federated learning
Iterative algorithms
Iterative methods
Knowledge sharing
Learning
Mutual learning
Optimization
Particle swarm optimization
Performance evaluation
Resource management
Training
ISSN:0018-9545, 1939-9359
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Popis
Shrnutí:Mutual learning (ML) is a promising technique for sharing knowledge in data while keeping the data privacy and preserving the individual characteristics of the local model. In this paper, we design a novel decentralized mutual learning (DML) system, where bidirectional device-to-device (D2D) communications are employed to facilitate the knowledge sharing. To accelerate the learning process and reduce the energy consumption at mobile devices, a non-convex optimization problem is formulated to minimize the average communication energy consumption for sharing knowledge among mobile devices. On this basis, a two-layer iterative algorithm is proposed, which consists of an outer layer algorithm based on the particle swarm optimization (PSO) method for searching a suitable user selection strategy and an inner layer algorithm based on sum-of-ratios optimization method to achieve a globally optimal allocation of communication resource for accelerating the learning process. Numerical results validate the fast convergence property and the effectiveness of the proposed algorithm, and the asynchronous nature of the designed system in terms of knowledge sharing and energy saving.
Bibliografia:ObjectType-Article-1
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2023.3299732