Class-incremental few-shot learning, where new sets of classes are provided sequentially
with only a few training samples, presents a great challenge due to catastrophic forgetting of …

Federated Learning (FL) enables model training directly on edge devices, thereby
enhancing privacy by keeping data local. Despite advances in FL, the dynamic nature of …

In the rapidly evolving field of machine learning, the adoption of traditional approaches often
encounters limitations, such as increased computational costs and the challenge of …

In this paper, we show that the process of continually learning new tasks and memorizing
previous tasks introduces unknown privacy risks and challenges to bound the privacy loss …

In recent years, the privacy-protecting framework Differential Privacy (DP) has achieved
remarkable success and has been widely studied. However, there is a lack of work on DP in …

The availability of large amounts of labeled training data is a major contributing factor (and a
bottleneck) to the recent progress in the field of Deep Learning. However, collecting and …

Nowadays, machine learning (ML) becomes ubiquitous and it is transforming society.
However, there are still many incidents caused by ML-based systems when ML is deployed …