Tight feasibility thresholds are derived for the minimal sub-optimality gap in convex L-smooth distributed optimization under bounded adversarial gradient perturbations, together with algorithms attaining them at matching query complexity.
Bilmes, and Jure Leskovec
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Distributed Learning with Adversarial Gradient Perturbations
Tight feasibility thresholds are derived for the minimal sub-optimality gap in convex L-smooth distributed optimization under bounded adversarial gradient perturbations, together with algorithms attaining them at matching query complexity.