Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway
Due to the long train marshaling and complex line conditions, the operating modes in heavy haul rail systems frequently change when trains travel. Improper traction or braking operation made by drivers will increase the longitudinal impact force to trains and causes the train decoupling, severely af...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9184854/ |
| _version_ | 1818662501276975104 |
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| author | Gaofeng Hua Li Zhu Jinsong Wu Chunzi Shen Linyan Zhou Qingqing Lin |
| author_facet | Gaofeng Hua Li Zhu Jinsong Wu Chunzi Shen Linyan Zhou Qingqing Lin |
| author_sort | Gaofeng Hua |
| collection | DOAJ |
| description | Due to the long train marshaling and complex line conditions, the operating modes in heavy haul rail systems frequently change when trains travel. Improper traction or braking operation made by drivers will increase the longitudinal impact force to trains and causes the train decoupling, severely affecting the safe operations of trains. It is quite desirable to replace the manual control with intelligent control in heavy haul rail systems. Traditional machine learning-based intelligent control methods suffer from insufficient data. Due to lacking effective incentives and trust, data from different rail lines or operators cannot be shared directly. In this paper, we propose an approach on blockchain-based federated learning to implement asynchronous collaborative machine learning between distributed agents that own data. This method performs distributed machine learning without a trusted central server. The blockchain smart contract is used to realize the management of the entire federated learning. Using the historical driving data collected from real heavy haul rail systems, the learning agent in the federated learning method adopts a support vector machine (SVM) based intelligent control model. To deal with the imbalanced traction and braking data, we optimize the classic SVM model via assigning different penalty factors to the majority and minority classes. The data set are mapped to a high dimension using kernel functions to make it linearly separable. We construct a mixing kernel function composed of polynomial and radial basis function (RBF) kernel functions, which uses a dynamic weight factor changing with train speeds to improve the model accuracy. The simulation results demonstrate the efficiency and accuracy of our proposed intelligent control method. |
| first_indexed | 2024-12-17T05:01:57Z |
| format | Article |
| id | doaj.art-79c3b89ef06a4aa89c19496979edb60c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:01:57Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-79c3b89ef06a4aa89c19496979edb60c2022-12-21T22:02:32ZengIEEEIEEE Access2169-35362020-01-01817683017683910.1109/ACCESS.2020.30212539184854Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul RailwayGaofeng Hua0Li Zhu1https://orcid.org/0000-0003-3688-1658Jinsong Wu2https://orcid.org/0000-0003-4720-5946Chunzi Shen3Linyan Zhou4Qingqing Lin5State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, ChinaSchool of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, ChinaHuawei Beijing Research Institute, Beijing, ChinaDue to the long train marshaling and complex line conditions, the operating modes in heavy haul rail systems frequently change when trains travel. Improper traction or braking operation made by drivers will increase the longitudinal impact force to trains and causes the train decoupling, severely affecting the safe operations of trains. It is quite desirable to replace the manual control with intelligent control in heavy haul rail systems. Traditional machine learning-based intelligent control methods suffer from insufficient data. Due to lacking effective incentives and trust, data from different rail lines or operators cannot be shared directly. In this paper, we propose an approach on blockchain-based federated learning to implement asynchronous collaborative machine learning between distributed agents that own data. This method performs distributed machine learning without a trusted central server. The blockchain smart contract is used to realize the management of the entire federated learning. Using the historical driving data collected from real heavy haul rail systems, the learning agent in the federated learning method adopts a support vector machine (SVM) based intelligent control model. To deal with the imbalanced traction and braking data, we optimize the classic SVM model via assigning different penalty factors to the majority and minority classes. The data set are mapped to a high dimension using kernel functions to make it linearly separable. We construct a mixing kernel function composed of polynomial and radial basis function (RBF) kernel functions, which uses a dynamic weight factor changing with train speeds to improve the model accuracy. The simulation results demonstrate the efficiency and accuracy of our proposed intelligent control method.https://ieeexplore.ieee.org/document/9184854/Federated learningblockchainsupport vector machineradial basis functionheavy haul railway |
| spellingShingle | Gaofeng Hua Li Zhu Jinsong Wu Chunzi Shen Linyan Zhou Qingqing Lin Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway IEEE Access Federated learning blockchain support vector machine radial basis function heavy haul railway |
| title | Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway |
| title_full | Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway |
| title_fullStr | Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway |
| title_full_unstemmed | Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway |
| title_short | Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway |
| title_sort | blockchain based federated learning for intelligent control in heavy haul railway |
| topic | Federated learning blockchain support vector machine radial basis function heavy haul railway |
| url | https://ieeexplore.ieee.org/document/9184854/ |
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