Ship AIS Trajectory Prediction Algorithm Based on Federated Learning

doi:10.19678/j.issn.1000-3428.0067829

Abstract

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)

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

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.

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.

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Figures/Tables 10

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

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

Fig.3 Comparison of loss values during the training process

Fig.4 Comparison of training times

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

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