Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence
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 industrial robots' decision making. In this article, to solve this problem, we propose an efficient and privacy-enhanced federated learning (PEFL) scheme for IAI. Compared with existing solutions, PEFL is noninteractive, and can prevent private data from being leaked even if multiple entities collude with each other. Moreover, extensive experiments with real-world data demonstrate the superiority of PEFL in terms of accuracy and efficiency.
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FL can play an important role in establishing intelligent caching policies to cope with the explosive growth of mobile data in modern IoT networks.
“IoT data offloaded from mobile devices can be cached by edge servers [65] where FL can play an important role in establishing intelligent caching policies, in order to cope with the explosive growth of mobile data in modern IoT networks.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [65]
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Self-citation
- Authors
- Hao, M., Li, H., Luo, X., Xu, G., Yang, H., & Liu, S.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Soochow University (CN)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2020-10
- Volume
- 16
- Issue
- 10
- Pages
- 6532-6542
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2977797911
- Cited by
- 569
- References
- 41
- Citations by year
- 2026: 33 · 2025: 74 · 2024: 111 · 2023: 102 · 2022: 105 · 2021: 91 · 2020: 51 · 2019: 2
- FWCI
- 28.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- National Key R&D Program of China (2017YFB0802300, 2017YFB0802000)
- National Natural Science Foundation of China (61972454, 61802051, 61772121, 61728102, 61472065)
- Peng Cheng Laboratory Project of Guangdong Province (PCL2018KP004)
- Guangxi Key Laboratory of Cryptography and Information Security (GCIS201804)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- federated learning, privacy preservation, industrial AI, collusion resistance, data loss prevention, Industry 4.0, medical data privacy, autonomous driving, wearable devices, industrial robots
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