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Deep Federated Lear...
Deep Federated Learning Enhanced Secure POI Microservices for Cyber-Physical Systems
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- Guo, Zhiwei (author)
- Chongqing Technol & Business Univ, Chongqing, Peoples R China.
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- Yu, Keping (author)
- Hosei Univ, Tokyo, Japan.;RIKEN Ctr Adv Intelligence Project, Tokyo, Japan.
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- Lv, Zhihan (author)
- Uppsala universitet,Institutionen för speldesign
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- Choo, Kim-Kwang Raymond (author)
- Univ Texas San Antonio, San Antonio, TX USA.
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- Shi, Peng (author)
- Univ Adelaide, Adelaide, SA, Australia.
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- Rodrigues, Joel J. P. C. (author)
- China Univ Petr East China, Coll Comp Sci & Technol, Dongying, Peoples R China.;Inst Telecomun, Lisbon, Portugal.
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Chongqing Technol & Business Univ, Chongqing, Peoples R China Hosei Univ, Tokyo, Japan.;RIKEN Ctr Adv Intelligence Project, Tokyo, Japan. (creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- English.
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In: IEEE wireless communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1284 .- 1558-0687. ; 29:2, s. 22-29
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- An essential consideration in cyber-physical systems (CPS) is the ability to support secure communication services, such as points of interest (POI) microservices. Existing approaches to support secure POI microservices generally rely on anonymity and/or differential privacy technologies. There are, however, a number of known limitations with such approaches. Hence, this work presents a deep-federated-learning-based framework for securing POI microservices in CPS. In order to enhance data security, the system architecture is designed to isolate the cloud center from accessing user data on edge nodes, and an interactive training mechanism is introduced between the cloud center and edge nodes. Specifically, edge nodes pre-train reliable deep-learning-based models for users, and the cloud server coordinates parameter updating via federated learning. The connected and isolated structure between cloud center and edges facilitates deep federated learning. Finally, we implement and evaluate the performance of our proposed approach using two real-world POI-related datasets. The results show that our proposed approach achieves optimal scheduling performance and demonstrates its practical utility.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Training
- Privacy
- Microservice architectures
- Systems architecture
- Optimal scheduling
- Cyber-physical systems
- Collaborative work
- Network security
Publication and Content Type
- ref (subject category)
- art (subject category)
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