Zhenyong Zhang

FedE: Protecting Training Data of Federated Learning Based on Multi-Precision Functional Encryption

FedE, a multi-precision, multi-source, heterogeneous privacy-preserving federated learning training method based on functional encryption that enhances numerical adaptation during ciphertext computation and prevents model parameter updates from easily compromising privacy in cross-institutional federated learning.