GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation

Author:

Zhang Zhiwen1ORCID,Wang Hongjun1ORCID,Fan Zipei1ORCID,Chen Jiyuan1ORCID,Song Xuan1ORCID,Shibasaki Ryosuke1

Affiliation:

1. Department of Computer Science and Engineering and Research Institute of Trustworthy Autonomous Systems, SUSTech–UTokyo Joint Research Center on Super Smart City, Southern University of Science and Technology, Shenzhen, China

Funder

National Key Research and Development Project of China

Guangdong Provincial Key Laboratory

Grant in-Aid for Scientific Research B

Japan’s Ministry of Education, Culture, Sports, Science, and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Reference65 articles.

1. Multi-task Representation Learning for Travel Time Estimation

2. Learning to Estimate the Travel Time

3. IDLG: Improved deep leakage from gradients;zhao;arXiv 2001 02610,2020

4. Inverting gradients—How easy is it to break privacy in federated learning?;geiping;arXiv 2003 14053,2020

5. Deep leakage from gradients;zhu;Advances in neural information processing systems,2019

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Data-Driven Methods for Travel Time Estimation: A Survey;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24

2. Entropy to Mitigate Non-IID Data Problem on Federated Learning for the Edge Intelligence Environment;IEEE Access;2023

3. Generative Personalized Federated Learning Framework for Travel Time Estimation;Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems;2022-11-06

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