计算机工程2024，Vol.50Issue(2)：298-307,10.DOI:10.19678/j.issn.1000-3428.0067829
基于联邦学习的船舶AIS轨迹预测算法
Ship AIS Trajectory Prediction Algorithm Based on Federated Learning
摘要
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.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Key words
federated learning/ship trajectory prediction/Automatic Identification System(AIS)/deep learning/Non Independent and Identically Distributed(Non-IID)分类
计算机与自动化引用本文复制引用
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):298-307,10.基金项目
国家自然科学基金(62072146) （62072146）
浙江省重点研发计划项目(2021C03187) （2021C03187）
浙江省自然科学基金(LQ23F020015). （LQ23F020015）
