3 papers
cs.DC2025
CoFormer: Collaborating with Heterogeneous Edge Devices for Scalable Transformer Inference
Guanyu Xu, Zhiwei Hao, Li Shen +5
The impressive performance of transformer models has sparked the deployment of intelligent applications on resource-constrained edge devices. However, ensuring high-quality service…
cs.LG2024
Joint Input and Output Coordination for Class-Incremental Learning
Shuai Wang, Yibing Zhan, Yong Luo +4
Incremental learning is nontrivial due to severe catastrophic forgetting. Although storing a small amount of data on old tasks during incremental learning is a feasible solution, c…
cs.LG2024
Federated Learning with Only Positive Labels by Exploring Label Correlations
Xuming An, Dui Wang, Li Shen +5
Federated learning aims to collaboratively learn a model by using the data from multiple users under privacy constraints. In this paper, we study the multi-label classification pro…