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Byzantine-robust federated learning no longer needs to trade off convergence speed and model utility, even with a large number of malicious clients.
Dataset condensation, already vulnerable to backdoor attacks, now faces a far stealthier threat: InkDrop leverages decision boundary uncertainty to hide malicious triggers, making detection significantly harder.
