Work

ESMFL: Efficient and Secure Models for Federated Learning

Year: 2020

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2009.01867

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

Institutions: Northeastern University, Duke University, University of Connecticut

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3083312158

Cites: 25

Cited by: 5

Related to: 10