On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning

TL;DR

This paper introduces FLICKER, a privacy-preserving framework utilizing homomorphic encryption to address class imbalance in federated learning, significantly improving model accuracy while maintaining data privacy.

Contribution

The paper proposes a novel homomorphic encryption-based method, FLICKER, for privately balancing datasets in federated learning to mitigate class imbalance issues.

Findings

Significant accuracy improvements on popular datasets.

Effective privacy preservation during data balancing.

Compatibility with existing federated learning setups.

Abstract

Class imbalance in training datasets can lead to bias and poor generalization in machine learning models. While pre-processing of training datasets can efficiently address both these issues in centralized learning environments, it is challenging to detect and address these issues in a distributed learning environment such as federated learning. In this paper, we propose FLICKER, a privacy preserving framework to address issues related to global class imbalance in federated learning. At the heart of our contribution lies the popular CKKS homomorphic encryption scheme, which is used by the clients to privately share their data attributes, and subsequently balance their datasets before implementing the FL scheme. Extensive experimental results show that our proposed method significantly improves the FL accuracy numbers when used along with popular datasets and relevant baselines.