Federated Graph Learning for Cross-Domain Recommendation

TL;DR

This paper introduces FedGCDR, a federated graph learning framework for cross-domain recommendation that enhances knowledge transfer while preserving privacy and mitigating negative transfer risks.

Contribution

The paper presents a novel federated graph learning framework with modules for privacy-preserving knowledge transfer and negative knowledge filtering in cross-domain recommendation.

Findings

FedGCDR outperforms state-of-the-art methods on Amazon dataset.

The framework effectively filters harmful knowledge and improves recommendation accuracy.

Extensive experiments validate the robustness and effectiveness of the proposed approach.

Abstract

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer across source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods.