Establishes that no defense works against linear-proportion poisoning with unbounded noise in regularization-based continual learning and proposes verification and robust defenses for infrequent or bounded attacks.
arXiv preprint arXiv:2207.05225 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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A data-centric survey of federated learning that ranks non-IID data traits by influence on convergence, links splitting protocols to real phenomena, and examines data-related defenses under clean and adversarial conditions.
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Theory of Continual Learning Against Data Poisoning Attacks
Establishes that no defense works against linear-proportion poisoning with unbounded noise in regularization-based continual learning and proposes verification and robust defenses for infrequent or bounded attacks.
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From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning
A data-centric survey of federated learning that ranks non-IID data traits by influence on convergence, links splitting protocols to real phenomena, and examines data-related defenses under clean and adversarial conditions.