FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things
Abstract
With the explosive network data due to the advanced development of the Internet of Things (IoT), the demand for multiparty computation is increasing. In addition, with the advent of future digital society, data have been gradually evolving into an effective virtual asset for sharing and usage. With the nature of the sensitivity, massiveness, fragmentation, and security of multiparty data computation in the IoT environment, we propose a secure data collaboration framework (FDC) based on federated deep-learning technology. The proposed framework can realize the secure collaboration of multiparty data computation on the premise that the data do not need to be transmitted out of their private data center. This framework is empowered by public data center, private data center, and the blockchain technology. The private data center is responsible for data governance, data registration, and data management. The public data center is used for multiparty secure computation. The blockchain paradigm is responsible for ensuring secure data usage and transmissions. A real IoT scenario is used to validate the effectiveness of the proposed framework.
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The proposed scheme in [57] lacks security protection design for cloud-based learning.
“A drawback of this proposed scheme is the lack of security protection design for cloud-based learning in the FL process.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [57]
Simulations with human movement datasets confirm high performance in training accuracy and high-security degrees for a wearable sensor network in the FLchain framework of [69].
“Simulations with human movement datasets confirm high performance in training accuracy and high-security degrees for a wearable sensor network.”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [69]
An FLchain framework is studied in [69] to achieve secure IoT data collaboration among IoT users.
“To achieve a secure IoT data collaboration among IoT users, an FLchain framework is also studied in [69] among private data center, a public data center, and a blockchain network.”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [69]
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- Authors
- Yin, B., Yin, H., Wu, Y., & Jiang, Z.
- Affiliations
- Beijing Information Science & Technology University (CN)
- Tsinghua University (CN)
- University of Exeter (GB)
- Venue
- IEEE Internet of Things Journal
- Published
- 2020-07
- Volume
- 7
- Issue
- 7
- Pages
- 6348-6359
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W2999469653
- Cited by
- 149
- References
- 5
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 23 · 2023: 30 · 2022: 31 · 2021: 31 · 2020: 13
- FWCI
- 10.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- National Basic Research Program of China (2016YFB1000102)
- National Natural Science Foundation of China (61972222, 61672318)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- federated deep learning, secure multi-party computation, Internet of Things, data collaboration, blockchain, data governance, data sharing, privacy-preserving computation
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