Fast-adapting and Privacy-preserving Federated Recommender System (2104.00919v3)

Abstract: In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem. Recent deep neural network (DNN)-based recommender system research have made significant progress in improving prediction accuracy, which is largely attributed to the access to a large amount of users' personal data collected from users' devices and then centrally stored in the cloud server. However, as there are rising concerns around the globe on user privacy leakage in the online platform, the public is becoming anxious by such abuse of user privacy. Therefore, it is urgent and beneficial to develop a recommender system that can achieve both high prediction accuracy and high degree of user privacy protection. To this end, we propose a DNN-based recommendation model called PrivRec running on the decentralized federated learning (FL) environment, which ensures that a user's data never leaves his/her during the course of model training. On the other hand, to better embrace the data heterogeneity commonly existing in FL, we innovatively introduce a first-order meta-learning method that enables fast in-device personalization with only few data points. Furthermore, to defense from potential malicious participant that poses serious security threat to other users, we develop a user-level differentially private DP-PrivRec model so that it is unable to determine whether a particular user is present or not solely based on the trained model. Finally, we conduct extensive experiments on two large-scale datasets in a simulated FL environment, and the results validate the superiority of our proposed PrivRec and DP-PrivRec.

• The paper presents PrivRec, a federated DNN that uses meta learning to rapidly adapt to user data heterogeneity for personalized recommendations.
• It employs a two-stage training approach by first learning privacy-relaxed item embeddings and then applying differential privacy on user-item interactions to mitigate accuracy loss.
• Experimental results show PrivRec outperforming state-of-the-art methods in both accuracy and privacy, confirmed on large-scale datasets with optimal FL hyperparameters.

An Overview of "Fast-Adapting and Privacy-Preserving Federated Recommender System"

The paper "Fast-Adapting and Privacy-Preserving Federated Recommender System" addresses the dual challenge of achieving high prediction accuracy while ensuring strong privacy protection in recommender systems. The authors present a deep neural network (DNN)-based recommendation model called PrivRec, designed to operate within a federated learning (FL) framework. This approach allows the model to train collectively across decentralized data stored on users' personal devices, eliminating the need to centralize sensitive user data—thereby addressing privacy concerns.

Key Contributions

1. Federated Learning and Privacy: PrivRec utilizes FL to maintain user data on local devices, thus aligning with privacy-by-design methodologies. The federated approach ensures that user data does not leave the personal device, significantly reducing the risk of misuse or leakage.
2. Meta-Learning for Personalization: To tackle data heterogeneity among users, PrivRec incorporates a first-order meta-learning method enabling quick on-device personalization even with minimal available data points. This feature addresses variability in data quality and scale across devices, aiming to improve user-centric recommendation quality.
3. Differential Privacy Enhancement: Beyond the inherent privacy of federated learning, the authors develop DP-PrivRec by embedding user-level differential privacy (DP) safeguards into the model. This feature is designed to prevent malicious participants from inferring the presence of specific users in the dataset through attacks, such as membership inference.
4. Two-Stage Training Approach: DP-PrivRec employs a two-stage training strategy to counteract the potential performance degradation inherent in adding noise for DP. Initially, item representations are learned in a privacy-relaxed manner. Subsequently, user-item interactions are modeled with DP constraints, leveraging well-initialized item embeddings to mitigate accuracy loss.

Experimental Evaluation

The proposed systems, PrivRec and DP-PrivRec, undergo extensive evaluation through experiments on two large-scale datasets, yielding promising results. Key findings include:

• Comparison with Baselines: PrivRec outperforms several state-of-the-art FL-based recommendation methods, including ones that leverage meta-learning or graph neural networks (e.g., FedGNN), by leveraging richer user and item side-information.
• Impact of Hyperparameters: Sensitivity analysis on federated learning hyperparameters, such as the number of local/global training rounds and sampled clients, reveals optimal configurations crucial for maintaining high accuracy under privacy-preserving constraints.
• Privacy Performance Trade-offs: While DP-PrivRec displays a performance drop relative to PrivRec due to DP noise addition, the paper shows that the two-stage training can effectively bridge this gap. The balance between privacy and performance, modulated by privacy budget, is thoroughly evaluated using a moments accountant method for formal DP guarantees.

Theoretical and Practical Implications

The research lays significant groundwork for implementing secure, adaptive recommender systems in genuine applications, such as mobile apps and e-commerce platforms. By combining federated learning, meta-learning, and differential privacy, the authors outlined an approach that does not compromise user data, ensuring user control over their privacy. Additionally, the presented methodologies could extend to other domains requiring privacy-preserving data analysis and personalized user experiences.

Future discussions in AI may further explore scalable deployment of such systems across more diverse hardware and network conditions, optimizing communication efficiency and enhancing robustness against more sophisticated adversarial threats. The integration of these technologies shapes an evolving landscape where user privacy and personalization coexist harmoniously.