IP2FL: Interpretation-Based Privacy-Preserving Federated Learning for Industrial Cyber-Physical Systems-Reference-Cited by-同舟云学术

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IP2FL: Interpretation-Based Privacy-Preserving Federated Learning for Industrial Cyber-Physical Systems

Published:2024 Issue: Volume:2 Page:321-330
ISSN:2832-7004
Container-title:IEEE Transactions on Industrial Cyber-Physical Systems
language:
Short-container-title:Trans. Ind. Cyb-Phy. Sys.

Author:

Namakshenas Danyal¹^ORCID,Yazdinejad Abbas¹^ORCID,Dehghantanha Ali¹^ORCID,Parizi Reza M.²^ORCID,Srivastava Gautam³^ORCID

Affiliation:

1. Cyber Science Lab, Canada Cyber Foundry, University of Guelph, Guelph, ON, Canada

2. Decentralized Science Lab, College of Computing and Software Engineering, Kennesaw State University, Marietta, GA, USA

3. Department of Math and Computer Science, Brandon University, Manitoba, MB, Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/9984155/10356795/10614890.pdf?arnumber=10614890

Reference50 articles.

1. A Survey and Perspective on Industrial Cyber-Physical Systems (ICPS): From ICPS to AI-Augmented ICPS

2. Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

3. AP2FL: Auditable Privacy-Preserving Federated Learning Framework for Electronics in Healthcare

4. Commodity market exposure to energy-firm distress: Evidence from the Colonial Pipeline ransomware attack

5. The SolarWinds hack: Lessons for international humanitarian organizations

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1. An Efficient Greedy Hierarchical Federated Learning Training Method Based on Trusted Execution Environments;Electronics;2024-09-06

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