Mitigating Group Bias in Federated Learning: Beyond Local Fairness

TL;DR

This paper investigates how local fairness strategies in federated learning influence global fairness, providing theoretical insights and a new algorithm that improves fairness without sacrificing accuracy.

Contribution

It offers a theoretical analysis linking local and global fairness and introduces a globally fair training method based on local summary statistics.

Findings

Global fairness can be achieved using local summary statistics.

The proposed method improves fairness while maintaining high accuracy.

Experimental results validate the effectiveness of the new approach.

Abstract

The issue of group fairness in machine learning models, where certain sub-populations or groups are favored over others, has been recognized for some time. While many mitigation strategies have been proposed in centralized learning, many of these methods are not directly applicable in federated learning, where data is privately stored on multiple clients. To address this, many proposals try to mitigate bias at the level of clients before aggregation, which we call locally fair training. However, the effectiveness of these approaches is not well understood. In this work, we investigate the theoretical foundation of locally fair training by studying the relationship between global model fairness and local model fairness. Additionally, we prove that for a broad class of fairness metrics, the global model's fairness can be obtained using only summary statistics from local clients. Based on that, we propose a globally fair training algorithm that directly minimizes the penalized empirical loss. Real-data experiments demonstrate the promising performance of our proposed approach for enhancing fairness while retaining high accuracy compared to locally fair training methods.