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

• URL: http://arxiv.org/abs/2503.16233v2
• Date: Sat, 09 Aug 2025 23:41:23 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: We present the first unified large-scale empirical study of privacy-fairness-utility trade-offs in Federated Learning (FL)<n>We compare fairness-awares with Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Multi-Party Encryption (SMC)<n>We uncover unexpected interactions: DP mechanisms can negatively impact fairness, skew, and fairness-awares can inadvertently reduce privacy effectiveness.
• Score: 6.671649946926508
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) enables collaborative model training while preserving data privacy; however, balancing privacy preservation (PP) and fairness poses significant challenges. In this paper, we present the first unified large-scale empirical study of privacy-fairness-utility trade-offs in FL, advancing toward responsible AI deployment. Specifically, we systematically compare Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Multi-Party Computation (SMC) with fairness-aware optimizers including q-FedAvg, q-MAML, Ditto, evaluating their performance under IID and non-IID scenarios using benchmark (MNIST, Fashion-MNIST) and real-world datasets (Alzheimer's MRI, credit-card fraud detection). Our analysis reveals HE and SMC significantly outperform DP in achieving equitable outcomes under data skew, although at higher computational costs. Remarkably, we uncover unexpected interactions: DP mechanisms can negatively impact fairness, and fairness-aware optimizers can inadvertently reduce privacy effectiveness. We conclude with practical guidelines for designing robust FL systems that deliver equitable, privacy-preserving, and accurate outcomes.

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