Practical Distributed Learning: Secure Machine Learning with Communication-Efficient Local Updates
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Federated learning on edge devices poses new challenges arising from workers that misbehave, privacy needs, etc. We propose a new robust federated optimization algorithm, with 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.
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