Large-Scale Model Meets Federated Learning: A Hierarchical Hybrid Distributed Training Mechanism for Intelligent Intersection Large-Scale Model
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 continu...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020029 |
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| _version_ | 1850252278277079040 |
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| author | Chang Liu Shaoyong Guo Fangfang Dang Xuesong Qiu Sujie Shao |
| author_facet | Chang Liu Shaoyong Guo Fangfang Dang Xuesong Qiu Sujie Shao |
| author_sort | Chang Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-71f85ded888e4348be6e816df5c7c645 |
| institution | OA Journals |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-71f85ded888e4348be6e816df5c7c6452025-08-20T01:57:40ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-12-01741031104910.26599/BDMA.2024.9020029Large-Scale Model Meets Federated Learning: A Hierarchical Hybrid Distributed Training Mechanism for Intelligent Intersection Large-Scale ModelChang Liu0Shaoyong Guo1Fangfang Dang2Xuesong Qiu3Sujie Shao4State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaNetwork Security Center, State Grid Henan Electric Power Company Information Communication Branch, Zhengzhou 450052, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaThe 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.https://www.sciopen.com/article/10.26599/BDMA.2024.9020029intelligent intersectionslarge-scale modelsedge artificial intelligence (ai)federated learningcompressed sensing |
| spellingShingle | Chang Liu Shaoyong Guo Fangfang Dang Xuesong Qiu Sujie Shao Large-Scale Model Meets Federated Learning: A Hierarchical Hybrid Distributed Training Mechanism for Intelligent Intersection Large-Scale Model Big Data Mining and Analytics intelligent intersections large-scale models edge artificial intelligence (ai) federated learning compressed sensing |
| title | Large-Scale Model Meets Federated Learning: A Hierarchical Hybrid Distributed Training Mechanism for Intelligent Intersection Large-Scale Model |
| title_full | Large-Scale Model Meets Federated Learning: A Hierarchical Hybrid Distributed Training Mechanism for Intelligent Intersection Large-Scale Model |
| title_fullStr | Large-Scale Model Meets Federated Learning: A Hierarchical Hybrid Distributed Training Mechanism for Intelligent Intersection Large-Scale Model |
| title_full_unstemmed | Large-Scale Model Meets Federated Learning: A Hierarchical Hybrid Distributed Training Mechanism for Intelligent Intersection Large-Scale Model |
| title_short | Large-Scale Model Meets Federated Learning: A Hierarchical Hybrid Distributed Training Mechanism for Intelligent Intersection Large-Scale Model |
| title_sort | large scale model meets federated learning a hierarchical hybrid distributed training mechanism for intelligent intersection large scale model |
| topic | intelligent intersections large-scale models edge artificial intelligence (ai) federated learning compressed sensing |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020029 |
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