# PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning

Source: [PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning](<https://www.sota2.com/research/paper/68fa6d4b1e209131cea38d80>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:30:35.914Z

Authors: Sikha Pentyala, Nicola Neophytou, Anderson Nascimento, Martine De Cock, Golnoosh Farnadi

Published: 2022-05-23T19:26:12.000Z

## Abstract

Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity. Achieving group fairness in Federated Learning (FL) is challenging because mitigating bias inherently requires using the sensitive attribute values of all clients, while FL is aimed precisely at protecting privacy by not giving access to the clients' data. As we show in this paper, this conflict between fairness and privacy in FL can be resolved by combining FL with Secure Multiparty Computation (MPC) and Differential Privacy (DP). In doing so, we propose a method for training group-fair ML models in cross-device FL under complete and formal privacy guarantees, without requiring the clients to disclose their sensitive attribute values.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Object Detection on FACET (IID client shards)](<https://www.sota2.com/research/sota/object-detection-on-facet-iid-client-shards.md>): Object Detection; FACET (IID client shards); Overall mAP: 61.54.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2205.11584>)

[PDF](<https://arxiv.org/pdf/2205.11584.pdf>)