ESMFL: Efficient and Secure Models for Federated Learning

Sheng Lin; Chenghong Wang; Hongjia Li; Jieren Deng; Yanzhi Wang; Caiwen Ding

ESMFL: Efficient and Secure Models for Federated Learning

Cryptography and Security 2021-03-05 v2 Computer Vision and Pattern Recognition Machine Learning

Authors: Sheng Lin , Chenghong Wang , Hongjia Li , Jieren Deng , Yanzhi Wang , Caiwen Ding

Abstract

Nowadays, Deep Neural Networks are widely applied to various domains. However, massive data collection required for deep neural network reveals the potential privacy issues and also consumes large mounts of communication bandwidth. To address these problems, we propose a privacy-preserving method for the federated learning distributed system, operated on Intel Software Guard Extensions, a set of instructions that increase the security of application code and data. Meanwhile, the encrypted models make the transmission overhead larger. Hence, we reduce the commutation cost by sparsification and it can achieve reasonable accuracy with different model architectures.

Keywords

federated learning distributed learning federated graph learning

Cite

@article{arxiv.2009.01867,
  title  = {ESMFL: Efficient and Secure Models for Federated Learning},
  author = {Sheng Lin and Chenghong Wang and Hongjia Li and Jieren Deng and Yanzhi Wang and Caiwen Ding},
  journal= {arXiv preprint arXiv:2009.01867},
  year   = {2021}
}

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7 pages, 3 figures, accepted by NeurIPS Workshop 2020, SpicyFL

ESMFL: Efficient and Secure Models for Federated Learning

Abstract

Keywords

Cite

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