FedFDP: Fairness-Aware Federated Learning with Differential Privacy

TL;DR

FedFDP is a novel federated learning algorithm that balances fairness, differential privacy, and model accuracy through innovative gradient clipping and adaptive privacy techniques, outperforming existing methods.

Contribution

The paper introduces FedFDP, combining fairness-aware federated learning with differential privacy, including new gradient clipping and adaptive methods to optimize fairness and privacy trade-offs.

Findings

FedFDP outperforms state-of-the-art methods in fairness and accuracy.

The proposed gradient clipping technique effectively balances fairness and privacy.

Adaptive clipping reduces privacy budget consumption while maintaining performance.

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.