FAIR-FATE: Fair Federated Learning with Momentum
- URL: http://arxiv.org/abs/2209.13678v2
- Date: Sun, 2 Jul 2023 18:35:14 GMT
- Title: FAIR-FATE: Fair Federated Learning with Momentum
- Authors: Teresa Salazar, Miguel Fernandes, Helder Araujo, Pedro Henriques Abreu
- Abstract summary: We propose a novel FAIR FederATEd Learning algorithm that aims to achieve group fairness while maintaining high utility.
To the best of our knowledge, this is the first approach in machine learning that aims to achieve fairness using a fair Momentum estimate.
Experimental results on real-world datasets demonstrate that FAIR-FATE outperforms state-of-the-art fair Federated Learning algorithms.
- Score: 0.41998444721319217
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While fairness-aware machine learning algorithms have been receiving
increasing attention, the focus has been on centralized machine learning,
leaving decentralized methods underexplored. Federated Learning is a
decentralized form of machine learning where clients train local models with a
server aggregating them to obtain a shared global model. Data heterogeneity
amongst clients is a common characteristic of Federated Learning, which may
induce or exacerbate discrimination of unprivileged groups defined by sensitive
attributes such as race or gender. In this work we propose FAIR-FATE: a novel
FAIR FederATEd Learning algorithm that aims to achieve group fairness while
maintaining high utility via a fairness-aware aggregation method that computes
the global model by taking into account the fairness of the clients. To achieve
that, the global model update is computed by estimating a fair model update
using a Momentum term that helps to overcome the oscillations of non-fair
gradients. To the best of our knowledge, this is the first approach in machine
learning that aims to achieve fairness using a fair Momentum estimate.
Experimental results on real-world datasets demonstrate that FAIR-FATE
outperforms state-of-the-art fair Federated Learning algorithms under different
levels of data heterogeneity.
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