SLSGD: Secure and Efficient Distributed On-device Machine Learning

Authors: Cong Xie, Sanmi Koyejo, Indranil Gupta
Published: 2019-03-16 | Updated: 2019-10-01

Source: https://arxiv.org/abs/1903.06996

PDF: https://arxiv.org/pdf/1903.06996

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ロバスト最適化 連合学習 SGDの特性

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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.