FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning

• URL: http://arxiv.org/abs/2510.20250v1
• Date: Thu, 23 Oct 2025 06:10:11 GMT
• Title: FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
• Authors: Zhiqin Yang, Yonggang Zhang, Chenxin Li, Yiu-ming Cheung, Bo Han, Yixuan Yuan,
• Abstract summary: Federated Learning (FL) confronts a significant challenge known as data heterogeneity, which impairs model performance and convergence.<n>We propose textbfFedGPS, a novel framework that seamlessly integrates statistical distribution and gradient information from others.
• Score: 103.45987800174724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) confronts a significant challenge known as data heterogeneity, which impairs model performance and convergence. Existing methods have made notable progress in addressing this issue. However, improving performance in certain heterogeneity scenarios remains an overlooked question: \textit{How robust are these methods to deploy under diverse heterogeneity scenarios?} To answer this, we conduct comprehensive evaluations across varied heterogeneity scenarios, showing that most existing methods exhibit limited robustness. Meanwhile, insights from these experiments highlight that sharing statistical information can mitigate heterogeneity by enabling clients to update with a global perspective. Motivated by this, we propose \textbf{FedGPS} (\textbf{Fed}erated \textbf{G}oal-\textbf{P}ath \textbf{S}ynergy), a novel framework that seamlessly integrates statistical distribution and gradient information from others. Specifically, FedGPS statically modifies each client's learning objective to implicitly model the global data distribution using surrogate information, while dynamically adjusting local update directions with gradient information from other clients at each round. Extensive experiments show that FedGPS outperforms state-of-the-art methods across diverse heterogeneity scenarios, validating its effectiveness and robustness. The code is available at: https://github.com/CUHK-AIM-Group/FedGPS.

Related papers

• Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
  Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
  arXiv  Detail & Related papers  (2026-02-01T05:54:59Z)
• FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity [2.298932494750101]
  Federated Learning (FL) provides decentralised model training, which effectively tackles problems such as distributed data and privacy preservation.<n>We propose a novel method, textitFedQuad, that explicitly optimises smaller intra-class variance and larger inter-class variance across clients.<n>Our approach minimises the distance between similar pairs while maximising the distance between negative pairs, effectively disentangling client data in the shared feature space.
  arXiv  Detail & Related papers  (2025-09-04T11:11:10Z)
• Reducing Spurious Correlation for Federated Domain Generalization [15.864230656989854]
  In open-world scenarios, global models may struggle to predict well on entirely new domain data captured by certain media. Existing methods still rely on strong statistical correlations between samples and labels to address this issue. We introduce FedCD, an overall optimization framework at both the local and global levels.
  arXiv  Detail & Related papers  (2024-07-27T05:06:31Z)
• FLASH: Federated Learning Across Simultaneous Heterogeneities [55.0981921695672]
  FLASH(Federated Learning Across Simultaneous Heterogeneities) is a lightweight and flexible client selection algorithm.<n>It outperforms state-of-the-art FL frameworks under extensive sources of Heterogeneities.<n>It achieves substantial and consistent improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-13T20:04:39Z)
• FedFed: Feature Distillation against Data Heterogeneity in Federated Learning [88.36513907827552]
  Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. We propose a novel approach called textbfFederated textbfFeature textbfdistillation (FedFedFed) FedFed partitions data into performance-sensitive features (i.e., greatly contributing to model performance) and performance-robust features (i.e., limitedly contributing to model performance) Comprehensive experiments demonstrate the efficacy of FedFed in promoting model performance.
  arXiv  Detail & Related papers  (2023-10-08T09:00:59Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• Gradient Masked Averaging for Federated Learning [24.687254139644736]
  Federated learning allows a large number of clients with heterogeneous data to coordinate learning of a unified global model. Standard FL algorithms involve averaging of model parameters or gradient updates to approximate the global model at the server. We propose a gradient masked averaging approach for FL as an alternative to the standard averaging of client updates.
  arXiv  Detail & Related papers  (2022-01-28T08:42:43Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)

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.