SLSGD: Secure and Efficient Distributed On-Device Machine Learning
Abstract
We consider distributed on-device learning with limited communication and security requirements. We propose a new robust distributed optimization algorithm with efficient communication and attack tolerance. The proposed algorithm has provable convergence and robustness under non-IID settings. Empirical results show that the proposed algorithm stabilizes the convergence and tolerates data poisoning on a small number of workers.
Cite
Text
Xie et al. "SLSGD: Secure and Efficient Distributed On-Device Machine Learning." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46147-8_13Markdown
[Xie et al. "SLSGD: Secure and Efficient Distributed On-Device Machine Learning." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/xie2019ecmlpkdd-slsgd/) doi:10.1007/978-3-030-46147-8_13BibTeX
@inproceedings{xie2019ecmlpkdd-slsgd,
title = {{SLSGD: Secure and Efficient Distributed On-Device Machine Learning}},
author = {Xie, Cong and Koyejo, Oluwasanmi and Gupta, Indranil},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2019},
pages = {213-228},
doi = {10.1007/978-3-030-46147-8_13},
url = {https://mlanthology.org/ecmlpkdd/2019/xie2019ecmlpkdd-slsgd/}
}