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Robust Federated Contrastive Recommender System to Mitigate Model Poisoning Attacks


Core Concepts
Contrastive learning can improve the performance of federated recommender systems, but also makes them more vulnerable to model poisoning attacks. A popularity-based contrastive regularizer is proposed to maintain the distance between item embeddings of different popularity levels, enhancing both the recommendation effectiveness and robustness of federated recommender systems.
Abstract
The paper introduces a contrastive learning framework tailored for federated recommender systems, called CL4FedRec. CL4FedRec constructs positive and negative user samples without compromising user privacy, and augments item views based on the updated user representations. Experiments show that CL4FedRec can significantly improve the recommendation performance of federated recommender systems. However, the authors find that contrastive learning also exacerbates the vulnerability of federated recommender systems to model poisoning attacks. This is attributed to the enhanced uniformity of the embedding distribution, which makes it easier for adversaries to manipulate target item embeddings to mimic popular items. To address this issue, the authors propose an enhanced and robust version of CL4FedRec, called rCL4FedRec, by introducing a popularity-based contrastive regularizer. This regularizer maintains the distance between item embeddings of different popularity levels, preventing adversaries from easily boosting the exposure of target items. Extensive experiments demonstrate that rCL4FedRec can significantly improve both the recommendation effectiveness and robustness of federated recommender systems against state-of-the-art model poisoning attacks.
Stats
The MovieLens-1M dataset contains 1,000,208 interaction records between 6,040 users and 3,706 movies. The Amazon-Phone dataset has 13,174 users, 5,970 cell phones, and 103,593 feedbacks. The Amazon-Video dataset contains 63,836 interactions involving 8,072 users and 11,830 items. The QB-Article dataset includes 10,981 users, 6,493 articles, and 335,663 reading records.
Quotes
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Deeper Inquiries

How can the proposed popularity-based contrastive regularizer be extended to other types of recommendation tasks beyond item recommendation, such as sequential recommendation or graph-based recommendation

The proposed popularity-based contrastive regularizer can be extended to other types of recommendation tasks beyond item recommendation by adapting it to suit the specific characteristics of the task. For sequential recommendation, the regularizer can be modified to consider the temporal aspect of the data. By incorporating time-based features or considering the sequence of interactions, the regularizer can ensure that the embeddings capture the sequential patterns effectively. In graph-based recommendation, the regularizer can be tailored to account for the graph structure. By incorporating graph-related features or leveraging graph embedding techniques, the regularizer can encourage the embeddings to capture the relationships and interactions between nodes in the graph. This adaptation would help improve the robustness of the model in capturing complex dependencies in graph-based recommendation tasks.

What are the potential limitations of the current contrastive learning framework in federated recommender systems, and how can they be addressed in future research

The current contrastive learning framework in federated recommender systems may have limitations in scenarios where the data sparsity is extreme or when the distribution of embeddings is highly skewed. In such cases, the contrastive learning task may struggle to effectively differentiate between positive and negative samples, leading to suboptimal performance. To address these limitations in future research, several approaches can be considered: Data Augmentation Techniques: Introducing more sophisticated data augmentation techniques tailored to the specific characteristics of federated data can help generate more diverse and informative views for contrastive learning. Adaptive Regularization: Incorporating adaptive regularization techniques that adjust the regularization strength based on the data distribution can help mitigate the impact of skewed distributions on the contrastive learning process. Hybrid Models: Exploring hybrid models that combine contrastive learning with other learning paradigms, such as self-supervised learning or generative modeling, can offer more robust and comprehensive representations in federated recommender systems. By addressing these limitations, future research can enhance the effectiveness and robustness of contrastive learning frameworks in federated recommender systems.

Given the enhanced robustness of rCL4FedRec, how can it be applied to other federated learning applications beyond recommender systems to improve their security against model poisoning attacks

The enhanced robustness of rCL4FedRec can be applied to other federated learning applications beyond recommender systems to improve their security against model poisoning attacks by adapting the regularizer to suit the specific requirements of the application. For example, in federated healthcare systems, rCL4FedRec can be utilized to protect patient data and ensure the integrity of medical models against adversarial attacks. By incorporating domain-specific constraints and privacy-preserving mechanisms, the regularizer can enhance the robustness of federated healthcare systems. In federated financial systems, rCL4FedRec can be employed to safeguard sensitive financial data and prevent malicious users from manipulating financial models for fraudulent activities. By integrating anomaly detection techniques and secure aggregation protocols, the regularizer can bolster the security of federated financial systems. By customizing the regularizer and defense mechanisms to the unique requirements of different federated learning applications, rCL4FedRec can serve as a versatile and effective defense strategy against model poisoning attacks in various domains.
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