Low-pass Personalized Subgraph Federated Recommendation

• URL: http://arxiv.org/abs/2603.20338v1
• Date: Fri, 20 Mar 2026 09:01:00 GMT
• Title: Low-pass Personalized Subgraph Federated Recommendation
• Abstract summary: Federated Recommender Systems (FRS) preserve privacy by training decentralized models on client-specific user-item subgraphs without sharing raw data.<n>We propose a Low-pass Personalized Subgraph Federated recommender system (LPSFed) to address subgraph structural imbalance.
• Score: 8.721619913104899
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Preference Data Selection for Mitigating the Alignment Tax in Large Language Models [52.18310687523229]
  BALIGN is a balanced data selection strategy that mitigates catastrophic forgetting while optimizing alignment efficacy.<n>By aggregating three key data-centric features into a unified composite risk score, BALIGN systematically filters out high-risk preference samples.<n>Experiments on standard human preference datasets demonstrate that BALIGN strongly preserves foundational capabilities without compromising alignment gains.
  arXiv  Detail & Related papers  (2026-08-25T07:58:42Z)
• Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients [22.724544289853558]
  FedSLM is a parameter-centric framework for federated fine-tuning with heterogeneous compressed clients.<n>We provide theoretical guarantees for adapter-level aggregation, subspace-alignment bounds for cross-group fusion, and a characterization of how the confidence loss mitigates weak-supervision noise.
  arXiv  Detail & Related papers  (2026-07-31T06:45:56Z)
• SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity [57.84199526590779]
  Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift that hampers global convergence.<n>In this work, we revisit client drift from a novel frequency-domain perspective and uncover a critical Spectral Bias of Drift.<n>Motivated by this, we propose SpecGradFilter, a unified Spectral Filtering Gradient Framework that tames heterogeneity by suppressing discordant low-frequency signals.
  arXiv  Detail & Related papers  (2026-07-05T09:23:35Z)
• Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs [77.42033827176806]
  One-Shot Federated Learning, where a central server learns a global model in a single communication round, has emerged as a promising paradigm.<n>We propose a novel Federated Model Inversion and Token Relabel framework, which trains the global model by fully exploiting all patches of synthetic images.
  arXiv  Detail & Related papers  (2026-05-11T15:49:08Z)
• FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection [51.473064583839296]
  We propose a novel Federated graph-level anomaly detection approach with Cluster-adaptIve GAted Reconstruction (FedCIGAR)<n>Specifically, we design a reconstruction-based paradigm trained on normal graphs to avoid synthetic data. Furthermore, we introduce a client-side node contribution gating mechanism and a server-side sliding window-based clustering strategy to tackle data heterogeneity.
  arXiv  Detail & Related papers  (2026-05-10T09:03:54Z)
• Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models [63.70401095689976]
  We argue that replacing parameters with preferences represents a more scalable and privacy-preserving future.<n>We propose MoR, a federated alignment framework based on GRPO with Mixture-of-Rewards for heterogeneous VLMs.<n>MoR consistently outperforms federated alignment baselines in generalization, robustness, and cross-client adaptability.
  arXiv  Detail & Related papers  (2026-01-31T03:11:51Z)
• Lightweight Inference-Time Personalization for Frozen Knowledge Graph Embeddings [0.0]
  GatedBias is a lightweight inference-time personalization framework for knowledge graphs.<n>Profile-specific features combine with graph-derived binary gates to produce interpretable, per-entity biases.<n>We evaluate GatedBias on two benchmark datasets.
  arXiv  Detail & Related papers  (2025-12-26T22:30:37Z)
• A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning [42.09768870974917]
  Federated Learning (FL) enables decentralized, privacy-preserving model training but struggles to balance global generalization and local personalization.<n>We propose adapting Linear Probing followed by full Fine-Tuning (LP-FT), a principled centralized strategy for alleviating feature distortion.
  arXiv  Detail & Related papers  (2025-11-16T17:19:23Z)
• Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework [57.04850867402913]
  Federated clustering addresses the challenge of extracting patterns from decentralized, unlabeled data.<n>We propose Structural Privacy-Preserving Federated Graph Clustering (SPP-FGC), a novel algorithm that innovatively leverages local structural graphs as the primary medium for privacy-preserving knowledge sharing.<n>Our framework achieves state-of-the-art performance, improving clustering accuracy by up to 10% (NMI) over federated baselines while maintaining provable privacy guarantees.
  arXiv  Detail & Related papers  (2025-11-14T03:05:22Z)
• CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks [51.43780477302533]
  Contribution-Oriented PFL (CO-PFL) is a novel algorithm that dynamically estimates each client's contribution for global aggregation.<n>CO-PFL consistently surpasses state-of-the-art methods in robustness in personalization accuracy, robustness, scalability and convergence stability.
  arXiv  Detail & Related papers  (2025-10-23T05:10:06Z)
• Curriculum Guided Personalized Subgraph Federated Learning [8.721619913104899]
  Subgraph Federated Learning (FL) aims to train Graph Neural Networks (GNNs) across distributed private subgraphs.<n> weighted model aggregation personalizes each local GNN by assigning larger weights to parameters from clients with similar subgraph characteristics.<n>We propose a novel personalized subgraph FL framework called Curriculum guided personalized sUbgraph Federated Learning (CUFL)
  arXiv  Detail & Related papers  (2025-08-30T08:01:36Z)
• FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling [36.314224807189575]
  We propose textitFedGrAINS, a novel data-adaptive and sampling-based regularization method for subgraph FL.<n>We show that the inclusion of textitFedGrAINS as a regularizer consistently improves the FL performance compared to baselines.
  arXiv  Detail & Related papers  (2025-01-22T02:35:20Z)
• FedImpro: Measuring and Improving Client Update in Federated Learning [77.68805026788836]
  Federated Learning (FL) models often experience client drift caused by heterogeneous data. We present an alternative perspective on client drift and aim to mitigate it by generating improved local models.
  arXiv  Detail & Related papers  (2024-02-10T18:14:57Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.