Energy-Aware, Device-to-Device Assisted Federated Learning in Edge Computing

The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and the rise of edge intelligence enables provisioning real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model train...

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Vydané v:IEEE transactions on parallel and distributed systems Ročník 34; číslo 7; s. 1 - 16
Hlavní autori: Li, Yuchen, Liang, Weifa, Li, Jing, Cheng, Xiuzhen, Yu, Dongxiao, Zomaya, Albert Y., Guo, Song
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Artificial neural networks
budgeted-energy DNN model training
Computational modeling
constrained optimization
Convergence
D2D-assisted learning
Data models
decentralized machine learning
Deep learning
Edge computing
Energy management
energy-aware federated learning
Federated learning
Heuristic methods
Internet of Things
Machine learning
Performance evaluation
Provisioning
Servers
Training
weighted maximum matching
ISSN:1045-9219, 1558-2183
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Popis
Shrnutí:The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and the rise of edge intelligence enables provisioning real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge computing environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this paper, we study energy-aware DNN model training in edge computing. We first formulate a novel energy-aware, Device-to-Device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and energy capacity on each IoT device. We then devise a near-optimal learning algorithm for the problem when the training data follows the i.i.d. data distribution. The crux of the proposed algorithm is to explore using the energy of neighboring devices of each device for its local model uploading, by reducing the problem to a series of weighted maximum matching problems in corresponding auxiliary graphs. We also consider the problem without the assumption of the i.i.d. data distribution, for which we propose an efficient heuristic algorithm. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the proposed algorithms are promising.
Bibliografia:ObjectType-Article-1
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2023.3277423