基于联邦学习的船舶AIS轨迹预测算法

doi:10.19678/j.issn.1000-3428.0067829

计算机工程 ›› 2024, Vol. 50 ›› Issue (2): 298-307. doi: 10.19678/j.issn.1000-3428.0067829

基于联邦学习的船舶AIS轨迹预测算法

郑晨俊¹, 曾艳¹^,²^,*(), 袁俊峰¹^,², 张纪林¹^,²^,³, 王鑫⁴, 韩猛¹

1. 杭州电子科技大学计算机学院, 浙江杭州 310018
2. 杭州电子科技大学复杂系统建模与仿真教育部重点实验室, 浙江杭州 310018
3. 数据安全治理浙江省工程研究中心, 浙江杭州 310018
4. 杭州电子科技大学圣光机联合学院, 浙江杭州 310018

收稿日期:2023-06-08 出版日期:2024-02-15 发布日期:2023-09-19
通讯作者: 曾艳
基金资助:
国家自然科学基金(62072146); 浙江省重点研发计划项目(2021C03187); 浙江省自然科学基金(LQ23F020015)

Ship AIS Trajectory Prediction Algorithm Based on Federated Learning

Chenjun ZHENG¹, Yan ZENG¹^,²^,*(), Junfeng YUAN¹^,², Jilin ZHANG¹^,²^,³, Xin WANG⁴, Meng HAN¹

1. School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China
2. Key Laboratory of Complex System Modeling and Simulation, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China
3. Zhejiang Engineering Research Center of Data Security Governance, Hangzhou 310018, Zhejiang, China
4. HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China

Received:2023-06-08 Online:2024-02-15 Published:2023-09-19
Contact: Yan ZENG

摘要/Abstract

摘要：

联邦学习是一种可以在弱通信环境下有效解决数据孤岛问题的分布式机器学习方法。针对海上船舶轨迹实时预测问题，提出基于Fedves联邦学习框架与卷积神经网络-门控循环单元(CNN-GRU)模型的船舶轨迹预测算法(E-FVTP)。根据Fedves联邦学习框架，通过规范客户端数据集规模以及客户端正则项，在保持原有客户端数据特征的前提下，减小数据非独立同分布特征对全局模型的影响，加快收敛速度。面向海洋通信资源短缺场景，建立基于船舶自动识别系统(AIS)数据的CNN-GRU船舶轨迹预测模型，解决了船舶终端设备计算能力不足的问题。在MarineCadastre开源和舟山海洋船舶航行AIS数据集上的实验结果表明，E-FVTP在预测误差比集中式训练降低40%的情况下，收敛速度提升67%、通信代价降低76.32%，可实现复杂海洋环境中船舶轨迹的精确预测，保障海上交通安全。

关键词: 联邦学习, 船舶轨迹预测, 自动识别系统, 深度学习, 非独立同分布

Abstract:

Federated learning, a distributed machine learning method, effectively addresses the data island problem in environments with weak communication. This study introduces an algorithm for predicting ship trajectories, employing the Fedves federated learning framework and a Convolutional Neural Network-Gated Recurrent Unit(CNN-GRU) model, called E-FVTP. The Fedves framework standardizes dataset sizes and client regularization terms, mitigating the influence of non-independent and identically distributed features on the global model. This approach preserves original client data features, thereby accelerating the convergence speed. In maritime scenarios with limited communication resources, the CNN-GRU model utilizes Automatic Identification System(AIS) data to overcome the computational limitations of vessel terminals. Experimental evaluations on the open-source MarineCadastre and Zhoushan marine ship navigation AIS datasets demonstrate that E-FVTP reduces prediction error by 40% compared to centralized training methods. It also achieves a 67% faster convergence rate and reduces communication costs by 76.32%. These advancements enable accurate vessel trajectory predictions in complex maritime settings, significantly ensuring maritime traffic safety.

Key words: federated learning, ship trajectory prediction, Automatic Identification System(AIS), deep learning, Non Independent and Identically Distributed(Non-IID)

郑晨俊, 曾艳, 袁俊峰, 张纪林, 王鑫, 韩猛. 基于联邦学习的船舶AIS轨迹预测算法[J]. 计算机工程, 2024, 50(2): 298-307.

Chenjun ZHENG, Yan ZENG, Junfeng YUAN, Jilin ZHANG, Xin WANG, Meng HAN. Ship AIS Trajectory Prediction Algorithm Based on Federated Learning[J]. Computer Engineering, 2024, 50(2): 298-307.

https://www.ecice06.com/CN/Y2024/V50/I2/298

图/表 10

图1 CNN-GRU船舶轨迹预测模型结构

Fig.1 Structure of CNN-GRU ship trajectory prediction model

图2 基于Fedves的船舶轨迹预测框架结构

Fig.2 Structure of ship trajectory prediction framework based on Fedves

图3 训练过程损失值对比

Fig.3 Comparison of loss values during the training process

图4 训练时间对比

Fig.4 Comparison of training times

图5 采样率对E-FVTP训练的影响

Fig.5 Impact of sampling rate on E-FVTP training

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