Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation

Konsep Inti

The authors propose a Federated Heterogeneous Graph Neural Network (FedHGNN) framework for privacy-preserving recommendations by partitioning the HIN into private and shared components, achieving up to 34% improvement in HR@10 and 42% in NDCG@10 under a reasonable privacy budget.

Abstrak

The paper introduces FedHGNN, a novel approach for privacy-preserving recommendations using a federated model. By partitioning the HIN into private and shared components, the proposed method outperforms existing models significantly. The two-stage perturbation mechanism ensures semantic preservation while protecting user privacy. Extensive experiments on real-world datasets demonstrate the effectiveness of FedHGNN.

Statistik

Extensive experiments show up to 34% improvement in HR@10 and 42% in NDCG@10.
The number of shared HINs is set as 20 for all datasets.

Kutipan

"The heterogeneous information network (HIN), which contains rich semantics depicted by meta-paths, has emerged as a potent tool for mitigating data sparsity in recommender systems."
"We suggest the HIN is partitioned into private HINs stored on the client side and shared HINs on the server."

Wawasan Utama Disaring Dari

Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation

by Bo Yan,Yang ... pada arxiv.org 02-29-2024

https://arxiv.org/pdf/2310.11730.pdf

Pertanyaan yang Lebih Dalam

How can the concept of differential privacy be further integrated into FedHGNN

Incorporating the concept of differential privacy further into FedHGNN can enhance the privacy guarantees provided by the model. One way to integrate this is by applying differential privacy mechanisms not only during the user-item interaction publishing process but also during the collaborative training phase. This would involve adding noise or perturbations to gradients and model parameters exchanged between clients and the server, ensuring that individual client data remains private even during model updates. By enforcing differential privacy throughout the entire federated learning process, FedHGNN can offer stronger assurances of data protection and confidentiality.

What are the potential implications of using pseudo-interacted items during local training

Using pseudo-interacted items during local training in FedHGNN serves as a form of regularization to improve model generalization and mitigate overfitting issues. These pseudo-interacted items are synthetic interactions generated for users based on their existing preferences or behavior patterns. By including these additional interactions in training, the model learns to generalize better across different user profiles and item categories, leading to improved recommendation performance on unseen data. However, it is essential to carefully tune parameters related to pseudo-interactions to prevent biasing the model towards specific types of recommendations.

How might the performance of FedHGNN vary across different types of recommendation systems

The performance of FedHGNN may vary across different types of recommendation systems based on factors such as dataset characteristics, sparsity levels, and domain-specific nuances:

For sparse datasets with limited user-item interactions like academic citation networks (e.g., ACM or DBLP), FedHGNN's ability to leverage rich semantics from heterogeneous information networks can significantly boost recommendation accuracy by capturing complex relationships between entities.
In e-commerce platforms such as Yelp or Douban Book where there is a wide range of item categories and diverse user preferences, FedHGNN's semantic-preserving approach combined with federated learning can lead to more personalized recommendations while maintaining user privacy.
The effectiveness of FedHGNN may be influenced by the availability and quality of meta-paths in each dataset; datasets with well-defined meta-paths that capture meaningful relationships among entities are likely to benefit more from HIN-based approaches like FedHGNN.
Overall, adapting Federated Heterogeneous Graph Neural Network (FedHGNN) for various recommendation scenarios requires careful consideration of dataset characteristics and tuning hyperparameters accordingly for optimal performance.