Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway

【Author】 Hua, Gaofeng; Zhu, Li; Wu, Jinsong; Shen, Chunzi; Zhou, Linyan; Lin, Qingqing

【Source】IEEE ACCESS

【Abstract】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.

【Keywords】Contracts; Training; Intelligent control; Rails; Rail transportation; Machine learning; Federated learning; blockchain; support vector machine; radial basis function; heavy haul railway

【标题】基于区块链的重载铁路智能控制联邦学习

【摘要】由于列车编组长、线路条件复杂，重载铁路系统的运行模式在列车行驶时经常发生变化。驾驶员牵引或制动操作不当，会增加对列车的纵向冲击力，导致列车脱钩，严重影响列车安全运行。在重载铁路系统中，用智能控制代替手动控制是非常可取的。传统的基于机器学习的智能控制方法存在数据不足的问题。由于缺乏有效的激励和信任，来自不同铁路线或运营商的数据无法直接共享。在本文中，我们提出了一种基于区块链的联邦学习方法，以在拥有数据的分布式代理之间实现异步协作机器学习。此方法在没有受信任的中央服务器的情况下执行分布式机器学习。区块链智能合约用于实现对整个联邦学习的管理。利用从真实重载铁路系统收集的历史驾驶数据，联邦学习方法中的学习代理采用基于支持向量机（SVM）的智能控制模型。为了处理不平衡的牵引和制动数据，我们通过为多数和少数类分配不同的惩罚因子来优化经典的 SVM 模型。使用核函数将数据集映射到高维，使其线性可分。我们构建了一个由多项式和径向基函数（RBF）核函数组成的混合核函数，它使用随列车速度变化的动态权重因子来提高模型精度。仿真结果证明了我们提出的智能控制方法的效率和准确性。

【关键词】合同；训练;智能控制；导轨；铁路运输；机器学习；联邦学习；区块链；支持向量机；径向基函数；重载铁路

【发表时间】2020

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】3.476

【翻译者】石东瑛