3 papers
cs.LG2026
FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching
He Yang, Dongyi Lv, Wei Xi +3
Most existing Byzantine-robust federated learning (FL) methods suffer from slow and unstable convergence. Moreover, when handling a substantial proportion of colluded malicious cli…
cs.LG2026
InkDrop: Invisible Backdoor Attacks Against Dataset Condensation
He Yang, Dongyi Lv, Song Ma +4
Dataset Condensation (DC) is a data-efficient learning paradigm that synthesizes small yet informative datasets, enabling models to match the performance of full-data training. How…
cs.CR2026
SNEAKDOOR: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation
He Yang, Dongyi Lv, Song Ma +2
Dataset condensation aims to synthesize compact yet informative datasets that retain the training efficacy of full-scale data, offering substantial gains in efficiency. Recent stud…