Federated fairness-aware classification under differential privacy

ArXi:2603.24392v1 Announce Type: cross 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 graphic disparity constrained classification under federated differential privacy, we propose a two-step algorithm, namely FDP-Fair.