Enforcing fairness in private federated learning via the modified method
of differential multipliers
- URL: http://arxiv.org/abs/2109.08604v1
- Date: Fri, 17 Sep 2021 15:28:47 GMT
- Title: Enforcing fairness in private federated learning via the modified method
of differential multipliers
- Authors: Borja Rodr\'iguez-G\'alvez and Filip Granqvist and Rogier van Dalen
and Matt Seigel
- Abstract summary: Federated learning with differential privacy, or private federated learning, provides a strategy to train machine learning models while respecting users' privacy.
This paper introduces an algorithm to enforce group fairness in private federated learning, where users' data does not leave their devices.
- Score: 1.3381749415517021
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated learning with differential privacy, or private federated learning,
provides a strategy to train machine learning models while respecting users'
privacy. However, differential privacy can disproportionately degrade the
performance of the models on under-represented groups, as these parts of the
distribution are difficult to learn in the presence of noise. Existing
approaches for enforcing fairness in machine learning models have considered
the centralized setting, in which the algorithm has access to the users' data.
This paper introduces an algorithm to enforce group fairness in private
federated learning, where users' data does not leave their devices. First, the
paper extends the modified method of differential multipliers to empirical risk
minimization with fairness constraints, thus providing an algorithm to enforce
fairness in the central setting. Then, this algorithm is extended to the
private federated learning setting. The proposed algorithm, FPFL, is tested on
a federated version of the Adult dataset and an "unfair" version of the FEMNIST
dataset. The experiments on these datasets show how private federated learning
accentuates unfairness in the trained models, and how FPFL is able to mitigate
such unfairness.
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