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PPSFL: Privacy-preserving Split Federated Learning via Functional Encryption
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  • Jing Ma ,
  • Lv Xixiang ,
  • Yong Yu ,
  • Stephan Sigg
Jing Ma
Xidian University

Corresponding Author:[email protected]

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Lv Xixiang
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Stephan Sigg
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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.