Privacy-enhanced ZKP-inspired Framework for Balanced Federated Learning

Federated learning (FL) is a distributed machine learning
approach that enables remote devices i.e. workers to collaborate to compute
the fitting of a neural network model without sharing their data.
While this method is favorable to ensure data privacy, an imbalanced
data distribution can introduce unfairness in the model training, causing
discriminatory bias towards certain under-represented groups. In this paper,
we show that imbalance federated data decreases indexes of equity
i.e. differences in treatment for underrepresented classes. To address the
problem, we propose a federated learning framework called Z-Fed that 1)
balances the training without exchange of privacy protected data using
a zero knowledge proof (ZKP) technique, and 2) allows for the collection
of information on data distributions based on one or more categorical
features to produce metadata about population proportions. The proposed
framework infers the precise data distribution without exchanging
knowledge of the data categories and uses it to coordinate a balanced
training set. Z-Fed aims to mitigate the effect of imbalanced data in
FL while respecting privacy and without using mediators or probabilistic
approaches. Compared to a non-balanced framework, Z-Fed improves
fairness and equality measured in equal opportunities (EPD) by 53.54%,
equal odds (EOD) by 56.41%, and statistical parity (SPD) by 46.1% on
imbalanced UTK datasets, reducing biased predictions among subgroups.
EPD, EOD, and SPD measure the disparity of treatment between privileged
e.g. over-represented and non-privileged groups. Given the results
obtained, Z-Fed can reduce discriminatory behaviors and enhance trustworthy
of federated learning.