Low-pass Personalized Subgraph Federated Recommendation
This work addresses privacy-preserving recommendation systems for decentralized clients, but it is incremental as it builds on existing federated learning methods with specific technical improvements.
The paper tackles the challenge of subgraph structural imbalance in Federated Recommender Systems, where variations in subgraph scale and connectivity misalign client representations, and proposes LPSFed, which uses low-pass spectral filtering and bias correction to achieve superior recommendation accuracy and robustness, validated on five real-world datasets.
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.