To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …

Abstract Federated Domain Generalization (FedDG) attempts to learn a global model in a
privacy-preserving manner that generalizes well to new clients possibly with domain shift …

Federated learning-based semantic segmentation (FSS) has drawn widespread attention
via decentralized training on local clients. However, most FSS models assume categories …

Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but
efficient synthetic training datasets from large ones. Existing DD methods based on gradient …

Abstract Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple
hospitals to collaborate distributedly without aggregating local data, thereby protecting …

In federated learning, a strong global model is collaboratively learned by aggregating
clients' locally trained models. Although this precludes the need to access clients' data …

Class-incremental learning (CIL) has achieved remarkable successes in learning new
classes consecutively while overcoming catastrophic forgetting on old categories. However …

Federated Learning (FL) is a distributed learning paradigm that collaboratively learns a
global model across multiple clients with data privacy-preserving. Although many FL …