Yuki Nakamura

Privacy-Preserving Feature Engineering for Federated Learning Analytics

This paper proposes a comprehensive framework for privacy-preserving feature engineering (PPFE) within federated learning analytics and explores techniques such as homomorphic encryption, differential privacy, and secure multi-party computation to enable robust, privacy-safe feature selection, transformation, and extraction across clients.