Privacy-Preserving Asynchronous Federated Learning Mechanism for Edge Network Computing
Open accessAbstract
In the traditional cloud architecture, data needs to be uploaded to the cloud for processing, bringing delays in transmission and response. Edge network emerges as the times require. Data processing on the edge nodes can reduce the delay of data transmission and improve the response speed. In recent years, the need for artificial intelligence of edge network has been proposed. However, the data of a single, individual edge node is limited and does not satisfy the conditions of machine learning. Therefore, performing edge network machine learning under the premise of data confidentiality became a research hotspot. This paper proposes a Privacy-Preserving Asynchronous Federated Learning Mechanism for Edge Network Computing (PAFLM), which can allow multiple edge nodes to achieve more efficient federated learning without sharing their private data. Compared with the traditional distributed learning, the proposed method compresses the communications between nodes and parameter server during the training process without affecting the accuracy. Moreover, it allows the node to join or quit in any process of learning, which can be suitable to the scene with highly mobile edge devices.
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FL with AI is more powerful thanks to its raw data protection method and local and global privacy levels.
“The FL with AI is more powerful thanks to its raw data protection method and local and global privacy levels [176].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [176]
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- Authors
- Lu, X., Liao, Y., Lio, P., & Hui, P.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Hong Kong University of Science and Technology (HK)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 48970-48981
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3010479380
- Cited by
- 159
- References
- 29
- Citations by year
- 2026: 12 · 2025: 18 · 2024: 33 · 2023: 30 · 2022: 31 · 2021: 26 · 2020: 9
- FWCI
- 9.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61472046)
- Ant Financial through the Ant Financial Science Funds for Security Research
- Beijing Association for Science and Technology Seed Fund
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- privacy-preserving federated learning, asynchronous federated learning, edge computing, data confidentiality, communication compression, parameter server, mobile edge devices
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