\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments

• URL: http://arxiv.org/abs/2605.07841v1
• Date: Fri, 08 May 2026 15:07:15 GMT
• Title: \mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments
• Abstract summary: Decentralized machine learning often relies on outsourcing computations, such as evaluations, to untrusted worker nodes.<n>We study this adversary-dominated setting through an incentive-oriented framework in which reports are accepted and rewarded only when they are mutually consistent up to a threshold.<n>We propose mathsfVISTA, an adaptive algorithm that tunes the acceptance threshold using the optimization history.
• Score: 21.69919643934826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decentralized machine learning often relies on outsourcing computations, such as gradient evaluations, to untrusted worker nodes. Existing robust aggregation methods can mitigate malicious behavior under honest-majority assumptions, but may fail when adversaries control a majority of the workers. We study this adversary-dominated setting through an incentive-oriented framework in which reports are accepted and rewarded only when they are mutually consistent up to a threshold. This turns the adversary from a pure saboteur into a rational agent that trades off increasing estimation error against the risk of rejection and loss of reward. We consider iterative optimization under this model. Unlike one-shot computation, iterative learning requires long-horizon decisions: permissive acceptance rules enable faster early progress but admit more adversarial corruption, while strict rules improve estimation accuracy but cause frequent rejections. We propose \mathsf{VISTA}, an adaptive algorithm that tunes the acceptance threshold using the optimization history. Numerical results show that \mathsf{VISTA} improves convergence over static thresholds. We also provide a rigorous convergence analysis showing that, with suitable incentive-aware adaptation, adversary-dominated decentralized learning can retain the asymptotic convergence behavior of standard SGD without relying on an honest majority.

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