3 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data
Xiufang Shi, Wei Zhang, Yuheng Li +5
In federated learning, the heterogeneity of client data has a great impact on the performance of model training. Many heterogeneity issues in this process are raised by non-indepen…
Exemplar-condensed Federated Class-incremental Learning
Rui Sun, Yumin Zhang, Varun Ojha +4
We propose Exemplar-Condensed federated class-incremental learning (ECoral) to distil the training characteristics of real images from streaming data into informative rehearsal exe…
Rehearsal-free Federated Domain-incremental Learning
Rui Sun, Haoran Duan, Jiahua Dong +3
We introduce a rehearsal-free federated domain incremental learning framework, RefFiL, based on a global prompt-sharing paradigm to alleviate catastrophic forgetting challenges in…
Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks
Zhenyu Liu, Haoran Duan, Huizhi Liang +5
Adversarial training is one of the most effective methods for enhancing model robustness. Recent approaches incorporate adversarial distillation in adversarial training architectur…