Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI

• URL: http://arxiv.org/abs/2503.16233v1
• Date: Thu, 20 Mar 2025 15:31:01 GMT
• Title: Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI
• Authors: Dawood Wasif, Dian Chen, Sindhuja Madabushi, Nithin Alluru, Terrence J. Moore, Jin-Hee Cho,
• Abstract summary: Federated Learning (FL) enables machine learning while preserving data privacy but struggles to balance privacy preservation (PP) and fairness.<n>DP enhances privacy but can disproportionately impact underrepresented groups, while HE and SMC fairness concerns at the cost of computational overhead.<n>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.
• Score: 6.671649946926508
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) enables collaborative machine learning while preserving data privacy but struggles to balance privacy preservation (PP) and fairness. Techniques like Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Multi-Party Computation (SMC) protect sensitive data but introduce trade-offs. DP enhances privacy but can disproportionately impact underrepresented groups, while HE and SMC mitigate fairness concerns at the cost of computational overhead. This work explores the privacy-fairness trade-offs in FL under IID (Independent and Identically Distributed) and non-IID data distributions, benchmarking q-FedAvg, q-MAML, and Ditto on diverse datasets. 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.

Related papers

• SAFES: Sequential Privacy and Fairness Enhancing Data Synthesis for Responsible AI [3.0445044300235535]
  We introduce SAFES, a Sequential PrivAcy and Fairness Enhancing data Synthesis procedure. For reasonable privacy loss, SAFES-generated synthetic data achieve significantly improved fairness metrics with relatively low utility loss.
  arXiv  Detail & Related papers  (2024-11-14T04:36:12Z)
• 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)
• 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)
• Libertas: Privacy-Preserving Collective Computation for Decentralised Personal Data Stores [18.91869691495181]
  We introduce a modular architecture, Libertas, to integrate MPC with PDS like Solid. We introduce a paradigm shift from an omniscient' view to individual-based, user-centric view of trust and security.
  arXiv  Detail & Related papers  (2023-09-28T12:07:40Z)
• 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)
• Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning [74.53077322713548]
  We investigate the privacy-preserving joint edge association and power allocation problem. The proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.
  arXiv  Detail & Related papers  (2023-01-26T10:09:23Z)
• 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)
• Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
  Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
  arXiv  Detail & Related papers  (2021-12-21T08:44:05Z)
• Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspective [10.901568085406753]
  Article is dedicated to surveying on the state-of-the-art privacy techniques, which can be employed in Federated learning. Recent research has demonstrated that retaining data and on computation in FL is not enough for privacy-guarantee. This is because ML model parameters exchanged between parties in an FL system, which can be exploited in some privacy attacks.
  arXiv  Detail & Related papers  (2020-11-10T21:41:25Z)
• Trustworthy AI [75.99046162669997]
  Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
  arXiv  Detail & Related papers  (2020-11-02T20:04:18Z)
• A Principled Approach to Data Valuation for Federated Learning [73.19984041333599]
  Federated learning (FL) is a popular technique to train machine learning (ML) models on decentralized data sources. The Shapley value (SV) defines a unique payoff scheme that satisfies many desiderata for a data value notion. This paper proposes a variant of the SV amenable to FL, which we call the federated Shapley value.
  arXiv  Detail & Related papers  (2020-09-14T04:37:54Z)

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.