FedRD: Privacy-preserving adaptive Federated learning framework for intelligent hazardous Road Damage detection and warning
| Autor: | Yachao Yuan, Dieter Hogrefe, Yali Yuan, Lutz M. Kolbe, Thar Baker |
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| Rok vydání: | 2021 |
| Předmět: |
Damage detection
Computer Networks and Communications Computer science Process (engineering) 020206 networking & telecommunications 02 engineering and technology Computer security computer.software_genre Federated learning Privacy preserving Information sensitivity Hardware and Architecture Hazardous waste 11. Sustainability 0202 electrical engineering electronic engineering information engineering Damages Differential privacy 020201 artificial intelligence & image processing computer Software |
| Zdroj: | Future Generation Computer Systems. 125:385-398 |
| ISSN: | 0167-739X |
| Popis: | Road damages have caused numerous fatalities. Therefore, the study of road damage detection, especially hazardous road damage detection and warning, is critical in improving traffic safety. Existing road damage detection systems mainly process data on clouds, however, they are not able to warn users timely due to the long latency. Recent edge-computing techniques mitigate this problem while users can only receive warnings of hazardous road damages within a small area due to the limited communication range of edges. Besides, untrusted edges might misuse users’ sensitive information. In this paper, we propose FedRD: a novel privacy-preserving edge-cloud and Federated learning-based framework for intelligent hazardous Road Damage detection and warning. In FedRD, a new hazardous road damage detection model is developed leveraging the advantages of hierarchical feature fusion. A novel adaptive federated learning strategy is designed for robust model learning from different edges with limited and unequally-sized datasets. A new individualized differential privacy approach with pixelization is proposed to protect users’ privacy before sharing data. Simulation results demonstrate that FedRD achieves a high detection performance and provides fast responses with accurate warning information covering a wider area while preserving users’ privacy, even when some edges have limited data. |
| Databáze: | OpenAIRE |
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