PPSFL: Privacy-preserving Split Federated Learning via Functional Encryption
Jing Ma, Xixiang Lv, Yong Hong Yu, Stephan Sigg · 2023
In this paper, we propose a novel and efficient privacy-preserving split federated learning (PPSFL) framework, that achieves both privacy protection and model accuracy with reasonable computational and communication cost. We describe the implementations of PPSFL on Multi-layer Perceptron (MLP) and Convolutional Neural Network (CNN) models with distributed clients to evaluate the performance of PPSFL.