A Study of Two-layer Asynchronous Federated Learning with Two-factor Updating for Vehicular Networking

WANG Lili; WU Shoulin; YANG Ni; HUANG Cheng

doi:10.11999/JEIT230918

Volume 46 Issue 7

Jul. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2842-2849

WANG Lili, WU Shoulin, YANG Ni, HUANG Cheng. A Study of Two-layer Asynchronous Federated Learning with Two-factor Updating for Vehicular Networking[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2842-2849. doi: 10.11999/JEIT230918

Citation:

WANG Lili, WU Shoulin, YANG Ni, HUANG Cheng. A Study of Two-layer Asynchronous Federated Learning with Two-factor Updating for Vehicular Networking[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2842-2849. doi: 10.11999/JEIT230918

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A Study of Two-layer Asynchronous Federated Learning with Two-factor Updating for Vehicular Networking

doi: 10.11999/JEIT230918

Institute of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

Received Date: 2023-08-23
Rev Recd Date: 2024-01-11

Available Online: 2024-01-15

Publish Date: 2024-07-29

Abstract

Abstract

In response to the characteristics of heterogeneous node resources and dynamic changes in the network topology in the Internet of Vehicles (IoV), a Two-layer Asynchronous Federated Learning with Two-factor updating (TTAFL) framework is established in this paper. Considering the impact of model version differences and the number of times that vehicles participate in Federated Learning (FL) on server model updates, a model update scheme based on staleness factor and contribution factor is proposed. Furthermore, to avoid the problem of roadside unit switching caused by vehicle mobility during the training process, a node selection scheme considering the residence time is given. Finally, in order to reduce the accuracy loss and system energy consumption, a reinforcement learning method is used to optimize the number of local iterations of FL and the number of local model updates of roadside units. Simulation results show that the proposed algorithm effectively improves the training efficiency and training accuracy of federated learning and reduces the system energy consumption.
- Internet of Vehicles (IoV),
- Federated Learning (FL),
- Asynchronous training,
- Deep reinforcement learning

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References(15)

References

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