# FedFDP: Fairness-Aware Federated Learning with Differential Privacy

arXiv: https://arxiv.org/abs/2402.16028 · HTML: https://mlsanity.com/paper/2402.16028/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2024.

## Built on by (most cited first, 3 of 3)

- [AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias](https://mlsanity.com/paper/2407.19655/) (2024)
- [A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data](https://mlsanity.com/paper/2411.07889/) (2024)
- [FUSE: Fairness-aware Update Screening against Byzantine Exploitation in q-Fair Federated Learning](https://mlsanity.com/paper/2610.20264/) (2026)

## Abstract (arXiv)

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.