Privacy-preserving Cost-sensitive Federated Learning from Imbalanced Data

Xiaowei Liu, Yuanzhi Yao, Yuting Ma, Nenghai Yu

Abstract: Federated learning allows multiple clients to collab-oratively train a global deep learning model without revealing their local data to a centralized server. However, the existence of clients whose datasets have imbalanced class distribution has a significant impact on model accuracy. Imbalance makes it challenging for a model to distinguish between the majority and minority classes without accessing clients' local data. In this paper, we aim to tackle this problem by privacy-preserving cost-sensitive federated learning. We design the joint cost-sensitive and differentially private model parameter optimization mechanism which maintains the model accuracy while satisfying differential privacy constraints. Moreover, this mechanism does not alter the original data distribution. Experimental results demonstrate the superior performance of our proposed scheme in terms of model accuracy and privacy preservation.