Distributed Learning with Adversarial Gradient Perturbations

Nawapon Sangsiri; Yufei Tao

IJCAI 20260 citations

Distributed Learning with Adversarial Gradient Perturbations

Abstract

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. Our study focuses on two fundamental questions: (i) what is the smallest achievable sub-optimality gap (i.e., excess error in optimization) under such responses, and (ii) how many queries are sufficient to guarantee a given sub-optimality gap? We establish tight feasibility thresholds on the sub-optimality gap and provide algorithms that achieve these thresholds with provable query complexity guarantees.

Machine Learning: Adversarial machine learningMachine Learning: Federated learning

BibTeX

@inproceedings{ijcai2026_distributedlearn,
  title = {Distributed Learning with Adversarial Gradient Perturbations},
  author = {Nawapon Sangsiri and Yufei Tao},
  booktitle = {IJCAI 2026},
  year = {2026}
}