FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

• URL: http://arxiv.org/abs/2502.17748v1
• Date: Tue, 25 Feb 2025 00:56:47 GMT
• Title: FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
• Authors: Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki,
• Abstract summary: FinP is a framework designed to achieve fairness in privacy by mitigating disproportionate exposure to source inference attacks.<n>FinP employs a dual approach: (1) server-side adaptive aggregation to address unfairness in client contributions in global model, and (2) client-side regularization to reduce client vulnerability.
• Score: 7.752864126266439
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring fairness in machine learning, particularly in human-centric applications, extends beyond algorithmic bias to encompass fairness in privacy, specifically the equitable distribution of privacy risk. This is critical in federated learning (FL), where decentralized data necessitates balanced privacy preservation across clients. We introduce FinP, a framework designed to achieve fairness in privacy by mitigating disproportionate exposure to source inference attacks (SIA). FinP employs a dual approach: (1) server-side adaptive aggregation to address unfairness in client contributions in global model, and (2) client-side regularization to reduce client vulnerability. This comprehensive strategy targets both the symptoms and root causes of privacy unfairness. Evaluated on the Human Activity Recognition (HAR) and CIFAR-10 datasets, FinP demonstrates ~20% improvement in fairness in privacy on HAR with minimal impact on model utility, and effectively mitigates SIA risks on CIFAR-10, showcasing its ability to provide fairness in privacy in FL systems without compromising performance.

Related papers

• Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation [60.81109086640437]
  We propose a novel framework called Federated Retrieval-Augmented Generation (FedE4RAG) FedE4RAG facilitates collaborative training of client-side RAG retrieval models. We apply homomorphic encryption within federated learning to safeguard model parameters.
  arXiv  Detail & Related papers  (2025-04-27T04:26:02Z)
• RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility in Autonomous Vehicles [6.3338980105224145]
  Existing FL frameworks struggle to balance privacy, fairness, and robustness, leading to performance disparities across demographic groups. This work explores the trade-off between privacy and fairness in FL-based object detection for AVs and introduces RESFL, an integrated solution optimizing both. RESFL incorporates adversarial privacy disentanglement and uncertainty-guided fairness-aware aggregation. We evaluate RESFL on the FACET dataset and CARLA simulator, assessing accuracy, fairness, privacy resilience, and robustness under varying conditions.
  arXiv  Detail & Related papers  (2025-03-20T15:46:03Z)
• Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI [6.671649946926508]
  Federated Learning (FL) enables machine learning while preserving data privacy but struggles to balance privacy preservation (PP) and fairness. DP enhances privacy but can disproportionately impact underrepresented groups, while HE and SMC fairness concerns at the cost of computational overhead. Our findings highlight context-dependent trade-offs and offer guidelines for designing FL systems that uphold responsible AI principles, ensuring fairness, privacy, and equitable real-world applications.
  arXiv  Detail & Related papers  (2025-03-20T15:31:01Z)
• PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data [47.745068077169954]
  Federated learning (FL) enables retailers to share model parameters for demand forecasting while maintaining privacy. We propose Privacy-Adaptive Clustered Federated Learning (PA-CFL) tailored for demand forecasting on heterogeneous retail data.
  arXiv  Detail & Related papers  (2025-03-15T18:07:54Z)
• FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning [17.853502904387376]
  Federated Learning (FL) enables collaborative training of models across distributed clients without sharing local data, addressing privacy concerns in decentralized systems. We propose Federated Error Minimization (FedEM), a novel algorithm that incorporates controlled perturbations through adaptive noise injection. Experimental results on benchmark datasets demonstrate that FedEM significantly reduces privacy risks and preserves model accuracy, achieving a robust balance between privacy protection and utility preservation.
  arXiv  Detail & Related papers  (2025-03-08T02:48:00Z)
• Convergent Differential Privacy Analysis for General Federated Learning: the $f$-DP Perspective [57.35402286842029]
  Federated learning (FL) is an efficient collaborative training paradigm with a focus on local privacy. differential privacy (DP) is a classical approach to capture and ensure the reliability of private protections.
  arXiv  Detail & Related papers  (2024-08-28T08:22:21Z)
• PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning [2.8304839563562436]
  Training and deploying Machine Learning models that simultaneously adhere to principles of fairness and privacy poses a significant challenge. We introduce PUFFLE, a high-level parameterised approach that can help in the exploration of the balance between utility, privacy, and fairness in FL scenarios. We prove that PUFFLE can be effective across diverse datasets, models, and data distributions, reducing the model unfairness up to 75%, with a maximum reduction in the utility of 17% in the worst-case scenario.
  arXiv  Detail & Related papers  (2024-07-21T17:22:18Z)
• FedFDP: Fairness-Aware Federated Learning with Differential Privacy [28.58589747796768]
  Federated learning (FL) is an emerging machine learning paradigm designed to address the challenge of data silos.<n>To tackle persistent issues related to fairness and data privacy, we propose a fairness-aware FL algorithm called FedFair.<n>Building on FedFair, we introduce differential privacy to create the FedFDP algorithm, which addresses trade-offs among fairness, privacy protection, and model performance.
  arXiv  Detail & Related papers  (2024-02-25T08:35:21Z)
• Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning [10.473137837891162]
  Federated Learning (FL) is a novel privacy-protection distributed machine learning paradigm. We propose a privacy-protection fairness FL method to protect the privacy of the client model. We conclude the relationship between privacy, fairness and utility, and there is a tradeoff between these.
  arXiv  Detail & Related papers  (2023-11-30T02:19:35Z)
• Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification [51.04894019092156]
  Federated learning (FL) has been recognized as a rapidly growing area, where the model is trained over clients under the FL orchestration (PS) In this paper, we propose a novel primal sparification algorithm for and guarantee non-smooth FL problems. Its unique insightful properties and its analyses are also presented.
  arXiv  Detail & Related papers  (2023-10-30T14:15:47Z)
• Fair Differentially Private Federated Learning Framework [0.0]
  Federated learning (FL) is a distributed machine learning strategy that enables participants to collaborate and train a shared model without sharing their individual datasets. Privacy and fairness are crucial considerations in FL. This paper presents a framework that addresses the challenges of generating a fair global model without validation data and creating a globally private differential model.
  arXiv  Detail & Related papers  (2023-05-23T09:58:48Z)
• PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy [56.347786940414935]
  Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation. This work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions.
  arXiv  Detail & Related papers  (2023-05-19T05:39:40Z)
• "You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning [78.70083858195906]
  We propose differentially private mechanisms to measure differences in performance across groups while protecting the privacy of group membership. Our results show that, contrary to what prior work suggested, protecting privacy is not necessarily in conflict with identifying performance disparities of federated models.
  arXiv  Detail & Related papers  (2022-06-24T09:46:43Z)
• PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning [12.767527195281042]
  Group fairness in Federated Learning (FL) is challenging because mitigating bias inherently requires using the sensitive attribute values of all clients. We show that this conflict between fairness and privacy in FL can be resolved by combining FL with Secure Multiparty Computation (MPC) and Differential Privacy (DP) In doing so, we propose a method for training group-fair ML models in cross-device FL under complete and formal privacy guarantees.
  arXiv  Detail & Related papers  (2022-05-23T19:26:12Z)
• Decentralized Stochastic Optimization with Inherent Privacy Protection [103.62463469366557]
  Decentralized optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since involved data, privacy protection has become an increasingly pressing need in the implementation of decentralized optimization algorithms.
  arXiv  Detail & Related papers  (2022-05-08T14:38:23Z)
• PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning [111.19576084222345]
  This paper proposes a framework of Privacy-preserving Credit risk modeling based on Adversarial Learning (PCAL) PCAL aims to mask the private information inside the original dataset, while maintaining the important utility information for the target prediction task performance. Results indicate that PCAL can learn an effective, privacy-free representation from user data, providing a solid foundation towards privacy-preserving machine learning for credit risk analysis.
  arXiv  Detail & Related papers  (2020-10-06T07:04:59Z)

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.