Fairness Regularization in Federated Learning

• URL: http://arxiv.org/abs/2508.12042v1
• Date: Sat, 16 Aug 2025 13:32:41 GMT
• Title: Fairness Regularization in Federated Learning
• Authors: Zahra Kharaghani, Ali Dadras, Tommy Löfstedt,
• Abstract summary: Federated Learning (FL) has emerged as a vital paradigm in modern machine learning.<n>This work focuses on performance equitable fairness, which aims to minimize differences in performance across clients.<n>We empirically show that FairGrad (approximate) and FairGrad* (exact) improve both fairness and overall model performance in heterogeneous data settings.
• Score: 1.4773243280881763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) has emerged as a vital paradigm in modern machine learning that enables collaborative training across decentralized data sources without exchanging raw data. This approach not only addresses privacy concerns but also allows access to overall substantially larger and potentially more diverse datasets, without the need for centralized storage or hardware resources. However, heterogeneity in client data may cause certain clients to have disproportionate impacts on the global model, leading to disparities in the clients' performances. Fairness, therefore, becomes a crucial concern in FL and can be addressed in various ways. However, the effectiveness of existing fairness-aware methods, particularly in heterogeneous data settings, remains unclear, and the relationships between different approaches are not well understood. In this work, we focus on performance equitable fairness, which aims to minimize differences in performance across clients. We restrict our study to fairness-aware methods that explicitly regularize client losses, evaluating both existing and newly proposed approaches. We identify and theoretically explain connections between the investigated fairness methods, and empirically show that FairGrad (approximate) and FairGrad* (exact) (two variants of a gradient variance regularization method introduced here for performance equitable fairness) improve both fairness and overall model performance in heterogeneous data settings.

Related papers

• Targeted Learning for Data Fairness [52.59573714151884]
  We expand fairness inference by evaluating fairness in the data generating process itself.<n>We derive estimators demographic parity, equal opportunity, and conditional mutual information.<n>To validate our approach, we perform several simulations and apply our estimators to real data.
  arXiv  Detail & Related papers  (2025-02-06T18:51:28Z)
• FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning [12.307490659840845]
  Federated Learning (FL) combines locally optimized models from various clients into a unified global model.<n>FL encounters significant challenges such as performance degradation, slower convergence, and reduced robustness of the global model.<n>We introduce an innovative dual-strategy approach designed to effectively resolve these issues.
  arXiv  Detail & Related papers  (2024-12-05T18:42:29Z)
• Achievable Fairness on Your Data With Utility Guarantees [16.78730663293352]
  In machine learning fairness, training models that minimize disparity across different sensitive groups often leads to diminished accuracy. We present a computationally efficient approach to approximate the fairness-accuracy trade-off curve tailored to individual datasets. We introduce a novel methodology for quantifying uncertainty in our estimates, thereby providing practitioners with a robust framework for auditing model fairness.
  arXiv  Detail & Related papers  (2024-02-27T00:59:32Z)
• FedGen: Generalizable Federated Learning for Sequential Data [8.784435748969806]
  In many real-world distributed settings, spurious correlations exist due to biases and data sampling issues. We present a generalizable federated learning framework called FedGen, which allows clients to identify and distinguish between spurious and invariant features. We show that FedGen results in models that achieve significantly better generalization and can outperform the accuracy of current federated learning approaches by over 24%.
  arXiv  Detail & Related papers  (2022-11-03T15:48:14Z)
• Straggler-Resilient Personalized Federated Learning [55.54344312542944]
  Federated learning allows training models from samples distributed across a large network of clients while respecting privacy and communication restrictions. We develop a novel algorithmic procedure with theoretical speedup guarantees that simultaneously handles two of these hurdles. Our method relies on ideas from representation learning theory to find a global common representation using all clients' data and learn a user-specific set of parameters leading to a personalized solution for each client.
  arXiv  Detail & Related papers  (2022-06-05T01:14:46Z)
• Uncertainty Minimization for Personalized Federated Semi-Supervised Learning [15.123493340717303]
  We propose a novel semi-supervised learning paradigm which allows partial-labeled or unlabeled clients to seek labeling assistance from data-related clients (helper agents) Experiments show that our proposed method can obtain superior performance and more stable convergence than other related works with partial labeled data.
  arXiv  Detail & Related papers  (2022-05-05T04:41:27Z)
• Fair and Consistent Federated Learning [48.19977689926562]
  Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively. We propose an FL framework to jointly consider performance consistency and algorithmic fairness across different local clients.
  arXiv  Detail & Related papers  (2021-08-19T01:56:08Z)
• Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
  dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
  arXiv  Detail & Related papers  (2021-04-14T14:20:22Z)
• Toward Understanding the Influence of Individual Clients in Federated Learning [52.07734799278535]
  Federated learning allows clients to jointly train a global model without sending their private data to a central server. We defined a new notion called em-Influence, quantify this influence over parameters, and proposed an effective efficient model to estimate this metric.
  arXiv  Detail & Related papers  (2020-12-20T14:34:36Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)

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.