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.
CITATION STYLE
Xie, C., Koyejo, O., & Gupta, I. (2020). SLSGD: Secure and Efficient Distributed On-device Machine Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11907 LNAI, pp. 213–228). Springer. https://doi.org/10.1007/978-3-030-46147-8_13
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