# Federated fairness-aware classification under differential privacy

arXiv: https://arxiv.org/abs/2603.24392 · HTML: https://mlsanity.com/paper/2603.24392/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 14 of 14)

- [Opacus: User-Friendly Differential Privacy Library in PyTorch](https://mlsanity.com/paper/2109.12298/) (2021)
- [Finite Sample Differentially Private Confidence Intervals](https://mlsanity.com/paper/1711.03908/) (2017)
- [Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems](https://mlsanity.com/paper/2103.06172/) (2021)
- [Making the Most of Parallel Composition in Differential Privacy](https://mlsanity.com/paper/2109.09078/) (2021)
- [Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing](https://mlsanity.com/paper/2402.02817/) (2024)
- [Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints](https://mlsanity.com/paper/2406.06755/) (2024)
- [Federated Transfer Learning with Differential Privacy](https://mlsanity.com/paper/2403.11343/) (2024)
- [Optimal estimation in private distributed functional data analysis](https://mlsanity.com/paper/2412.06582/) (2024)
- [Minimax Optimal Fair Classification with Bounded Demographic Disparity](https://mlsanity.com/paper/2403.18216/) (2024)
- [Finite-Sample and Distribution-Free Fair Classification: Optimal Trade-off Between Excess Risk and Fairness, and the Cost of Group-Blindness](https://mlsanity.com/paper/2410.16477/) (2024)
- [Optimal Cox regression under federated differential privacy: coefficients and cumulative hazards](https://mlsanity.com/paper/2508.19640/) (2025)
- [Fairness-aware Bayes optimal functional classification](https://mlsanity.com/paper/2505.09471/) (2025)
- [On Theoretical Limits of Learning with Label Differential Privacy](https://mlsanity.com/paper/2502.14309/) (2025)
- [Fairness Meets Privacy: Integrating Differential Privacy and Demographic Parity in Multi-class Classification](https://mlsanity.com/paper/2511.18876/) (2025)

## Abstract (arXiv)

Privacy and algorithmic fairness have become two central issues in modern machine learning. Although each has separately emerged as a rapidly growing research area, their joint effect remains comparatively under-explored. In this paper, we systematically study the joint impact of differential privacy and fairness on classification in a federated setting, where data are distributed across multiple servers. Targeting demographic disparity constrained classification under federated differential privacy, we propose a two-step algorithm, namely FDP-Fair. In the special case where there is only one server, we further propose a simple yet powerful algorithm, namely CDP-Fair, serving as a computationally-lightweight alternative. Under mild structural assumptions, theoretical guarantees on privacy, fairness and excess risk control are established. In particular, we disentangle the source of the private fairness-aware excess risk into a) intrinsic cost of classification, b) cost of private classification, c) non-private cost of fairness and d) private cost of fairness. Our theoretical findings are complemented by extensive numerical experiments on both synthetic and real datasets, highlighting the practicality of our designed algorithms.