FedFDP: Fairness-Aware Federated Learning with Differential Privacy

• URL: http://arxiv.org/abs/2402.16028v4
• Date: Mon, 02 Dec 2024 03:17:08 GMT
• Title: FedFDP: Fairness-Aware Federated Learning with Differential Privacy
• Authors: Xinpeng Ling, Jie Fu, Kuncan Wang, Huifa Li, Tong Cheng, Zhili Chen, Haifeng Qian, Junqing Gong,
• Abstract summary: 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.
• Score: 28.58589747796768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) is an emerging machine learning paradigm designed to address the challenge of data silos, attracting considerable attention. However, FL encounters persistent issues related to fairness and data privacy. To tackle these challenges simultaneously, we propose a fairness-aware federated learning algorithm called FedFair. Building on FedFair, we introduce differential privacy to create the FedFDP algorithm, which addresses trade-offs among fairness, privacy protection, and model performance. In FedFDP, we developed a fairness-aware gradient clipping technique to explore the relationship between fairness and differential privacy. Through convergence analysis, we identified the optimal fairness adjustment parameters to achieve both maximum model performance and fairness. Additionally, we present an adaptive clipping method for uploaded loss values to reduce privacy budget consumption. Extensive experimental results show that FedFDP significantly surpasses state-of-the-art solutions in both model performance and fairness.

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