Defending Federated Recommender Systems against Untargeted Attacks: A Contribution-Aware Robust Aggregation Scheme

Authors

Feida Zhu (ORCID: https://orcid.org/0000-0001-6077-4356)
Yuanchun Jiang (ORCID: https://orcid.org/0000-0003-0886-3647)
Ruicheng Liang (ORCID: https://orcid.org/0000-0001-6266-2657)
Ling Cheng (ORCID: https://orcid.org/0000-0002-2834-9728)
Huiwen Liu (ORCID: https://orcid.org/0009-0003-2363-9018)

Institutions

University of Science and Technology of China (CN)
Hefei University of Technology (CN)
Anhui University of Finance and Economics (CN)
Hefei University (CN)
Singapore Management University (SG)

Publication Details

Journal: ACM Transactions on Knowledge Discovery from Data
Published: 2024-11-28
DOI: https://doi.org/10.1145/3706112
Citations: 5
Primary Topic: Recommender Systems and Techniques
Type: article
Field-Weighted Citation Impact: 3.90

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article

Defending Federated Recommender Systems against Untargeted Attacks: A Contribution-Aware Robust Aggregation Scheme

Feida Zhu, Yuanchun Jiang, Ruicheng Liang, Ling Cheng et al.

Nov 28, 2024

5 citations

ACM Transactions on Knowledge Discovery from Data

Recommender Systems and Techniques

3.90

article

Defending Federated Recommender Systems against Untargeted Attacks: A Contribution-Aware Robust Aggregation Scheme

Feida Zhu, Yuanchun Jiang, Ruicheng Liang, Ling Cheng, Huiwen Liu

November 28, 2024article en

5 citations

Abstract

No abstract available for this paper.

ACM Transactions on Knowledge Discovery from DataVol. 19(1)

University of Science and Technology of China (CN), Hefei University of Technology (CN), Anhui University of Finance and Economics (CN), Hefei University (CN), Singapore Management University (SG)

Peace, Justice and strong institutions

Openalex Percentile: Top 5%

Recommender Systems and Techniques

3.90

DOI Open Access PDF OpenAlex

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