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A cutting-edge framework for industrial intrusion detection: Privacy-preserving, cost-friendly, and powered by federated learning

Published Online: 2025-04-02

Published Print: 2025-05

Authors

Zhu, Lingzi https://orcid.org/0009-0001-9822-0428
Zhao, Bo

Guo, Jiabao

Ji, Minzhi

Peng, Junru
Funding

Funding for this research was provided by:

Primary Research & Development Plan of Hubei Province (No.2020BAA003)
License Information

Text and Data Mining valid from 2025-04-02

Version of Record valid from 2025-04-02
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Article History

Accepted: 23 February 2025

First Online: 2 April 2025

Declarations

:

: The authors declare that they have no conflict of interest.

: Not applicable.

: Not applicable.

: All authors reviewed and provided consents for publication.

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