Cited By
View all- Gao HLiu Y(2022)FedDFA: Dual-Factor Aggregation for Federated Driver Distraction DetectionBig Data and Social Computing10.1007/978-981-19-7532-5_15(237-250)Online publication date: 7-Dec-2022
FedURR: a federated transfer learning framework for multi-department collaborative urban risk recognition
Authors: 
Hang Gao, Yi LiuAuthors Info & Claims
Article No.: 3, Pages 1 - 5
Abstract
Urban risk recognition is an essential task for urban public safety. Recent researches indicate that deep learning is playing an increasingly important role in urban risk recognition. This paper proposes FedURR, a federated transfer learning framework for multi-department collaborative urban risk recognition. The framework supports multiple departments to jointly train a neural network on the premise that the data is stored locally. Specifically, we introduce federated learning to ensure collaborative training, and well-designed transfer learning ensures that better performance can be achieved at a lower cost. Experimental results show that the proposed framework is effective and efficient.
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Index Terms
- FedURR: a federated transfer learning framework for multi-department collaborative urban risk recognition
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Published In
November 2021
18 pages
ISBN:9781450390996
DOI:10.1145/3486182
Copyright © 2021 ACM.
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Publication History
Published: 03 December 2021
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- Research-article
Funding Sources
- National Key R&D Program of China
- National Natural Science Foundation of China
Conference
SIGSPATIAL '21
Sponsor:
SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems
November 3, 2021
Beijing, China
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Overall Acceptance Rate 30 of 54 submissions, 56%
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Cited By
View all- Gao HLiu Y(2022)FedDFA: Dual-Factor Aggregation for Federated Driver Distraction DetectionBig Data and Social Computing10.1007/978-981-19-7532-5_15(237-250)Online publication date: 7-Dec-2022
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