Large-Scale Model Meets Federated Learning:A Hierarchical Hybrid Distributed Training Mechanism for Intelligent Intersection Large-Scale Model  

作　　者：Chang Liu Shaoyong Guo Fangfang Dang Xuesong Qiu Sujie Shao 

机构地区：[1]State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China [2]Network Security Center,State Grid Henan Electric Power Company Information Communication Branch,Zhengzhou 450052,China

出　　处：《Big Data Mining and Analytics》2024年第4期1031-1049,共19页大数据挖掘与分析(英文)

基　　金：supported by the National Natural Science Foundation of China(No.62322103);the BUPT Excellent PhD Students Foundation(No.CX2022218).

摘　　要：The large-scale model(LSM)can handle large-scale data and complex problems,effectively improving the intelligence level of urban intersections.However,the traffic conditions at intersections are becoming increasingly complex,so the intelligent intersection LSMs(I2LSMs)also need to be continuously learned and updated.The traditional cloud-based training method incurs a significant amount of computational and storage overhead,and there is a risk of data leakage.The combination of edge artificial intelligence and federated learning provides an efficient and highly privacy protected computing mode.Therefore,we propose a hierarchical hybrid distributed training mechanism for I2LSM.Firstly,relying on the intelligent intersection system for cloud-network-terminal integration,we constructed an I2LSM hierarchical hybrid distributed training architecture.Then,we propose a hierarchical hybrid federated learning(H2Fed)algorithm that combines the advantages of centralized federated learning and decentralized federated learning.Further,we propose an adaptive compressed sensing algorithm to reduce the communication overhead.Finally,we analyze the convergence of the H2Fed algorithm.Experimental results show that the H2Fed algorithm reduces the communication overhead by 21.6%while ensuring the accuracy of the model.

关 键 词：intelligent intersections large-scale models edge artificial intelligence(AI) federated learning compressed sensing 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O211.61[自动化与计算机技术—控制科学与工程]