Federated learning faces new hybrid Byzantine attacks targeting network pruning

Researchers have developed a novel hybrid Byzantine attack for federated learning that combines a sparse manipulation strategy with a slow-accumulating poisoning method. This approach aims to maximize disruption to the global model while remaining imperceptible to common detection mechanisms. The attack selectively targets sensitive parameters and gradually poisons updates over multiple rounds, demonstrating effectiveness against eight state-of-the-art defense strategies. AI

影响 Introduces a novel attack vector that could necessitate new defenses in federated learning systems.

排序理由 Academic paper detailing a new attack method for federated learning. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• neural network
• Federated Learning
• Kerem Özfatura
• sparse NNs

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