Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence
University of Electronic Science and Technology of China · Peng Cheng Laboratory · +1 more institution
Abstract
By leveraging deep learning-based technologies, industrial artificial intelligence (IAI) has been applied to solve various industrial challenging problems in Industry 4.0. However, for privacy reasons, traditional centralized training may be unsuitable for sensitive data-driven industrial scenarios, such as healthcare and autopilot. Recently, federated learning has received widespread attention, since it enables participants to collaboratively learn a shared model without revealing their local data. However, studies have shown that, by exploiting the shared parameters adversaries can still compromise industrial applications such as auto-driving navigation systems, medical data in wearable devices, and…
Citation impact
- FWCI
- 38.01
- Percentile
- 100%
- References
- 44
Authors
6- MHMeng HaoCorresponding
University of Electronic Science and Technology of China, Peng Cheng Laboratory
- HLHongwei Li
University of Electronic Science and Technology of China, Peng Cheng Laboratory
- XLXizhao Luo
Soochow University
- GXGuowen Xu
University of Electronic Science and Technology of China
- HYHaomiao Yang
University of Electronic Science and Technology of China
Topics & keywords
- Computer science
- Compromise
- Artificial intelligence
- Scheme (mathematics)
- Information privacy
- Wearable computer
- Wearable technology
- Big data