Federated fairness-aware classification under differential privacy
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 computa...
| Authors | Gengyu Xue, Yi Yu |
|---|---|
| Primary category | stat.ML |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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