Hanlin Gu

Federated learning struggles when data quality varies across clients, but FedQual solves this with a novel approach that calibrates low-quality clients while preserving high-quality autonomy.

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.