DRSGD-ByMI identifies Byzantine machines via sample-splitting score statistics with FDR control, then prunes them to recover sufficient connectivity and achieve order-optimal convergence rates identical to standard decentralized SGD.
Machine learning with adversaries: Byzantine tolerant gradient descent.Advances in neural information processing systems, 30
2 Pith papers cite this work. Polarity classification is still indexing.
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XFED is the first aggregation-agnostic non-collusive model poisoning attack that bypasses eight state-of-the-art defenses on six benchmark datasets without attacker coordination.
citing papers explorer
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Toward Exact Convergence in Byzantine-Robust Decentralized Learning: A Statistical Identification Approach
DRSGD-ByMI identifies Byzantine machines via sample-splitting score statistics with FDR control, then prunes them to recover sufficient connectivity and achieve order-optimal convergence rates identical to standard decentralized SGD.
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XFED: Non-Collusive Model Poisoning Attack Against Byzantine-Robust Federated Classifiers
XFED is the first aggregation-agnostic non-collusive model poisoning attack that bypasses eight state-of-the-art defenses on six benchmark datasets without attacker coordination.