A Traffic Flow Prediction Method Based on the Fusion of Blockchain and Federated Learning

ZHI Hui; DUAN Miaomiao; YANG Lixia; HUANG Yu; FEI Jie; WANG Yaning

doi:10.11999/JEIT240030

Volume 46 Issue 9

Sep. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(9): 3777-3787

ZHI Hui, DUAN Miaomiao, YANG Lixia, HUANG Yu, FEI Jie, WANG Yaning. A Traffic Flow Prediction Method Based on the Fusion of Blockchain and Federated Learning[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3777-3787. doi: 10.11999/JEIT240030

Citation:

ZHI Hui, DUAN Miaomiao, YANG Lixia, HUANG Yu, FEI Jie, WANG Yaning. A Traffic Flow Prediction Method Based on the Fusion of Blockchain and Federated Learning[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3777-3787. doi: 10.11999/JEIT240030

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A Traffic Flow Prediction Method Based on the Fusion of Blockchain and Federated Learning

doi: 10.11999/JEIT240030

ZHI Hui^{1, 2
,
,},
DUAN Miaomiao^{1, 2},
YANG Lixia¹,
HUANG Yu^{1, 2},
FEI Jie^{1, 2},
WANG Yaning^{1, 2}

1.
School of Electronic and Information Engineering, Anhui University, Hefei 230601, China
2.
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China

Funds: The National Natural Science Foundation of China (62001001, U21A20457, 62071003, 41874174, 61901004), Anhui Province high-tech Field Key Research and Development Program (202304a05020011), Anhui Graduate Education Quality Project (2023qygzz005), The Natural Science Research Project of Anhui University (2022AH050109), The Natural Science Foundation of Anhui Province (2008085MF186), Anhui University Collaborative Innovation Project (GXXT-2020-050), The National Defense Technology and Collaborative Innovation Project of Anhui University (GXXT-2021-028)

Received Date: 2024-01-19
Rev Recd Date: 2024-07-14

Available Online: 2024-08-02

Publish Date: 2024-09-26

Abstract

Abstract

In the field of intelligent transportation, real-time and accurate traffic flow prediction has always been the top priority in urban development, which plays a crucial role in improving the operation efficiency of the road network. Most of the existing traffic flow prediction methods are based on machine learning, ignoring cases where the client is unwilling to participate in the prediction task or lies in order to obtain high rewards, resulting in a decline in the accuracy of traffic flow prediction when the model is aggregated. This paper proposes a Traffic Flow Prediction Method Based on Blockchain and Federated Learning (TFPM-BFL) to solve this problem. In this method, the client uses the Long Short-Term Memory (LSTM) model with attention mechanism to make local prediction and improve the prediction accuracy. An incentive mechanism based on credit rating is designed. Local and local credit values are obtained by evaluating the quality of the model uploaded by the client, and rewards are distributed according to the credit rating results, so as to encourage the client to participate in federal learning. Edge Server (ES) uses the model aggregation method based on credit value and compression rate to improve the model aggregation quality. The simulation results show that TFPM-BFL can achieve accurate and timely traffic flow prediction, effectively motivate clients to participate in Federated Learning (FL) tasks while ensuring the privacy of underlying data, and realize high-quality model aggregation.
- Traffic flow prediction,
- Federated learning,
- Blockchain,
- Incentive mechanism

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References(30)

References

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