SLSGD: Secure and Efficient Distributed On-device Machine Learning
This addresses secure and efficient learning for distributed systems, but appears incremental as it builds on existing distributed optimization methods.
The paper tackles the problem of distributed on-device machine learning with communication and security constraints by proposing a robust optimization algorithm that stabilizes convergence and tolerates data poisoning on a small number of workers.
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.