解决联邦推荐中子图结构差异导致的模型偏差问题
Low-pass Personalized Subgraph Federated Recommendation
- 用图傅里叶变换提取跨子图稳定的低频结构信号
- 在五个数据集上准确率提升显著，鲁棒性更强
- 适合处理用户-物品子图规模和连通性差异大的场景
联邦推荐系统（FRS）通过在客户端本地的用户-物品子图上训练模型来保护隐私，但面临子图结构失衡的问题：子图规模（用户/物品数量）和连通性（物品度数）差异大，导致客户端表征不一致，难以训练出鲁棒模型。为此，我们提出低通个性化子图联邦推荐框架（LPSFed）。LPSFed利用图傅里叶变换与低通谱滤波，提取在不同规模和度数子图间保持稳定的低频结构信号，指导基于中性结构锚点相似性的个性化参数更新。此外，引入局部流行度偏差感知边界，捕捉每个子图内的物品度数不平衡，并将其融入个性化偏置校正项以缓解推荐偏差。理论分析支持该方法，且在五个真实世界数据集上验证了其优越的推荐精度与模型鲁棒性。
原文摘要 · Abstract (English)
Federated Recommender Systems (FRS) preserve privacy by training decentralized models on client-specific user-item subgraphs without sharing raw data. However, FRS faces a unique challenge: subgraph structural imbalance, where drastic variations in subgraph scale (user/item counts) and connectivity (item degree) misalign client representations, making it challenging to train a robust model that respects each client's unique structural characteristics. To address this, we propose a Low-pass Personalized Subgraph Federated recommender system (LPSFed). LPSFed leverages graph Fourier transforms and low-pass spectral filtering to extract low-frequency structural signals that remain stable across subgraphs of varying size and degree, allowing robust personalized parameter updates guided by similarity to a neutral structural anchor. Additionally, we leverage a localized popularity bias-aware margin that captures item-degree imbalance within each subgraph and incorporates it into a personalized bias correction term to mitigate recommendation bias. Supported by theoretical analysis and validated on five real-world datasets, LPSFed achieves superior recommendation accuracy and enhances model robustness.
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