Enforcing fairness in private federated learning via the modified method of differential multipliers. (arXiv:2109.08604v2 [cs.LG] UPDATED)

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 …

arxiv fairness federated learning learning