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Authors: Ruicheng Liang, Yuanchun Jiang, Feida Zhu, Ling Cheng, Huiwen Liu
ACM Transactions on Knowledge Discovery from Data
Accepted on 14 November 2024
Online AM: 28 November 2024 Publication History
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Abstract
Federated recommender systems (FedRSs) effectively tackle the trade-off between recommendation accuracy and privacy preservation. However, recent studies have revealed severe vulnerabilities in FedRSs, particularly against untargeted attacks seeking to undermine their overall performance. Defense methods employed in traditional recommender systems are not applicable to FedRSs, and existing robust aggregation schemes for other federated learning-based applications have proven ineffective in FedRSs. Building on the observation that malicious clients contribute negatively to the training process, we design a novel contribution-aware robust aggregation scheme to defend FedRSs against untargeted attacks, named contribution-aware Bayesian knowledge distillation aggregation (ConDA), comprising two key components for the defense. In the first contribution estimation component, we decentralize the estimation from the server side to the client side and propose an ensemble-based Shapley value to enable the efficient calculation of contributions, addressing the limitations of lacking auxiliary validation data and high computational complexity. In the second contribution-aware aggregation component, we merge the decentralized contributions via a majority voting mechanism and integrate the merged contributions into a Bayesian knowledge distillation aggregation scheme for robust aggregation, mitigating the impact of unreliable contributions induced by attacks. We evaluate the effectiveness and efficiency of ConDA on two real-world datasets from movie and music service providers. Through extensive experiments, we demonstrate the superiority of ConDA over the baseline robust aggregation schemes.
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Index Terms
Defending Federated Recommender Systems Against Untargeted Attacks: A Contribution-Aware Robust Aggregation Scheme
Information systems
Information retrieval
Retrieval tasks and goals
Information systems applications
Data mining
Index terms have been assigned to the content through auto-classification.
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EISSN:1556-472X
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Publication History
Online AM: 28 November 2024
Accepted: 14 November 2024
Revised: 23 August 2024
Received: 24 June 2023
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- Federated recommender systems
- untargeted attacks
- contribution estimation
- robust aggregation
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