Federated learning: frameworks, optimisation algorithms, security threats and defences

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Authors

Sen Wang (ORCID: https://orcid.org/0009-0009-7852-5513)
Yu Zhang (ORCID: https://orcid.org/0009-0002-6210-9469)
Ao Zhao (ORCID: https://orcid.org/0009-0009-3835-1870)
Hongyun Cai (ORCID: https://orcid.org/0000-0003-1175-429X)
Meiling Zhang (ORCID: https://orcid.org/0000-0002-8882-2564)

Publication Details

Journal: International Journal of Information and Computer Security
Published: 2025-01-01
DOI: https://doi.org/10.1504/ijics.2025.10073505
Primary Topic: Network Security and Intrusion Detection
Type: article
Field-Weighted Citation Impact: 0.00

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article

Federated learning: frameworks, optimisation algorithms, security threats and defences

Sen Wang, Yu Zhang, Ao Zhao, Hongyun Cai et al.

Jan 1, 2025

International Journal of Information and Computer Security

Network Security and Intrusion Detection

article

Federated learning: frameworks, optimisation algorithms, security threats and defences

Sen Wang, Yu Zhang, Ao Zhao, Hongyun Cai, Meiling Zhang

January 1, 2025article en

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

International Journal of Information and Computer SecurityVol. 28(3)

Openalex Percentile: Top 71%

Network Security and Intrusion Detection

DOI Source OpenAlex

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