Privacy-preserving gradient-based fair federated learning

TL;DR

This paper introduces a novel federated learning scheme that ensures fairness and privacy by exclusively using local gradients and homomorphic encryption, enhancing usability and potential control applications.

Contribution

The paper presents a new fair and privacy-preserving federated learning scheme based on homomorphic encryption that relies solely on local gradients, improving usability over existing methods.

Findings

Scheme ensures individual model fairness based on data contribution

Uses homomorphic encryption for privacy without sharing raw data

Enhances applicability in control systems

Abstract

Federated learning (FL) schemes allow multiple participants to collaboratively train neural networks without the need to directly share the underlying data.However, in early schemes, all participants eventually obtain the same model. Moreover, the aggregation is typically carried out by a third party, who obtains combined gradients or weights, which may reveal the model. These downsides underscore the demand for fair and privacy-preserving FL schemes. Here, collaborative fairness asks for individual model quality depending on the individual data contribution. Privacy is demanded with respect to any kind of data outsourced to the third party. Now, there already exist some approaches aiming for either fair or privacy-preserving FL and a few works even address both features. In our paper, we build upon these seminal works and present a novel, fair and privacy-preserving FL scheme. Our approach, which mainly relies on homomorphic encryption, stands out for exclusively using local gradients. This increases the usability in comparison to state-of-the-art approaches and thereby opens the door to applications in control.