Joint Optimization of Edge Selection and Resource Allocation in Digital Twin-assisted Federated Learning

TANG Lun; WEN Mingyan; SHAN Zhenzhen; CHEN Qianbin

doi:10.11999/JEIT230421

Volume 46 Issue 4

Apr. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(4): 1343-1352

TANG Lun, WEN Mingyan, SHAN Zhenzhen, CHEN Qianbin. Joint Optimization of Edge Selection and Resource Allocation in Digital Twin-assisted Federated Learning[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1343-1352. doi: 10.11999/JEIT230421

Citation:

TANG Lun, WEN Mingyan, SHAN Zhenzhen, CHEN Qianbin. Joint Optimization of Edge Selection and Resource Allocation in Digital Twin-assisted Federated Learning[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1343-1352. doi: 10.11999/JEIT230421

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Joint Optimization of Edge Selection and Resource Allocation in Digital Twin-assisted Federated Learning

doi: 10.11999/JEIT230421

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of mobile Communications Technology, Chongqing, 400065, China

Funds: The National Natural Science Foundation of China (62071078), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601), Sichuan Science and Technology Program (2021YFQ0053)

Received Date: 2023-05-15
Rev Recd Date: 2023-09-14

Available Online: 2023-09-15

Publish Date: 2024-04-24

Abstract

Abstract

In intelligent driving based on federated learning, the resource constraints of Intelligent Connected Vehicle (ICV) and possible device failures will lead to the decrease of the precision of federated learning training and the increase of delay and energy consumption. Therefore, an optimization scheme of edge selection and resource allocation in digital twin-assisted federated learning is proposed. Firstly, a digital twin-assisted federated learning mechanism is proposed, allowing ICV to choose to participate in federated learning locally or through its digital twin. Secondly, by constructing a computational and communication model for digital twin-assisted federated learning, an edge selection and computing resource allocation joint optimization problem is established with the objective of minimizing cumulative training delay and energy consumption, and is transformed into a partially observable Markov decision process. Finally, an edge selection and resource allocation algorithm based on Multi-agent Parametrized Deep Q-Networks (MPDQN) is proposed to learn approximately optimal edge selection and resource allocation strategies to minimize federated learning cumulative delay and energy consumption. Simulation results show that the proposed algorithm can effectively reduce cumulative training delay and energy consumption of federated learning training while ensuring model accuracy.
- Intelligent driving,
- Federated learning,
- Digital twin,
- Deep reinforcement learning

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References(15)

References

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