Distributed Learning with Adversarial Gradient Perturbations

ArXi:2605.03313v1 Announce Type: new Privacy concerns in distributed learning often lead clients to return intentionally altered gradient information. We consider the problem of learning convex and $L$-smooth functions under adversarial gradient perturbation, where a client's gradient reply to a server query can deviate arbitrarily from the true gradient subject to a distance bound.