Privacy and Fairness in Federated Learning: on the Perspective of Trade-off

TL;DR

This paper reviews the interplay between privacy and fairness in federated learning, highlighting their trade-offs, challenges, and potential solutions to guide future research in developing ethically balanced FL systems.

Contribution

It provides a comprehensive literature review of privacy and fairness in federated learning, emphasizing their interactions and proposing new directions for research.

Findings

Privacy and fairness often conflict in FL systems.

Existing solutions tend to focus on either privacy or fairness, not both.

The paper identifies key challenges and suggests future research directions.

Abstract

Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced, researchers have endeavored to devise FL systems that protect privacy or ensure fair results, with most research focusing on one or the other. As two crucial ethical notions, the interactions between privacy and fairness are comparatively less studied. However, since privacy and fairness compete, considering each in isolation will inevitably come at the cost of the other. To provide a broad view of these two critical topics, we presented a detailed literature review of privacy and fairness issues, highlighting unique challenges posed by FL and solutions in federated settings. We further systematically surveyed different interactions between privacy and fairness, trying to reveal how privacy and fairness could affect each other and point out new research directions in fair and private FL.