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Intell. Syst. Technol."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>The Transformer model, with its ability to capture long-term dependencies, has demonstrated significant potential in enhancing long-term traffic flow prediction for effective urban transportation management. However, most existing Transformer-based methods adhere to a centralized approach, failing to address privacy concerns and to optimize computational resource utilization. Although emerging federated learning paradigms offer privacy protection, the average aggregation still overlooks client heterogeneity and lacks the synchronous efficiency required for traffic flow prediction tasks. Consequently, we introduce FedTFormer, a hierarchical federated learning framework tailored to boost the performance of Transformer models in decentralized environments, involving clients, edge servers, and a central server. Initially, clients are organized into clusters through a sophisticated static clustering mechanism anchored in bipartite graph theory. FedTFormer enhances robustness of Transformer by facilitating synchronous average aggregation within clusters. Additionally, it performs asynchronous fine-tuning of cluster-specific parameters, leveraging hypernetwork constructed on the central server. Clients utilize an optimized Transformer model for localized training, harnessing its proficiency in capturing long-term spatio-temporal dependencies. Ultimately, we conduct extensive experiments across three datasets, comparing our method against ten sophisticated approaches and demonstrating the effectiveness and robustness of FedTFormer.<\/jats:p>","DOI":"10.1145\/3742792","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T11:45:12Z","timestamp":1748951112000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Hypernetwork-Enhanced Hierarchical Federated Learning for Long-Term Traffic Prediction with Transformer"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7968-2406","authenticated-orcid":false,"given":"Siyue","family":"Shuai","sequence":"first","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China and Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2698-3319","authenticated-orcid":false,"given":"Xiangjie","family":"Kong","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China and Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3693-196X","authenticated-orcid":false,"given":"Lutong","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China and Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3740-4829","authenticated-orcid":false,"given":"Wenhong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1064-1250","authenticated-orcid":false,"given":"Guojiang","family":"Shen","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China and Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2826-6367","authenticated-orcid":false,"given":"Ivan","family":"Lee","sequence":"additional","affiliation":[{"name":"STEM, University of South Australia, Adelaide, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"17","article-title":"FedAvg-P: Performance-based hierarchical federated learning-based anomaly detection system aggregation strategy for advanced metering infrastructure","volume":"24","author":"Alshede Hend","year":"2024","unstructured":"Hend Alshede, Kamal Jambi, Laila Nassef, Nahed Alowidi, and Etimad Fadel. 2024. 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