Federated fairness-aware classification under differential privacy

TL;DR

This paper explores the combined effects of differential privacy and fairness in federated learning, proposing new algorithms with theoretical guarantees and demonstrating their effectiveness through experiments.

Contribution

It introduces FDP-Fair and CDP-Fair algorithms for privacy-aware fair classification in federated settings, with theoretical analysis and empirical validation.

Findings

Algorithms achieve privacy and fairness guarantees.

Theoretical bounds on excess risk are established.

Experimental results confirm practical effectiveness.

Abstract

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.