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 …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

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 …

Deep learning models often suffer from forgetting previously learned information when
trained on new data. This problem is exacerbated in federated learning (FL), where the data …

While existing federated learning approaches mostly require that clients have fully-labeled
data to train on, in realistic settings, data obtained at the client-side often comes without any …

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 …

Large scale language models (LLM) have received significant attention and found diverse
applications across various domains, but their development encounters challenges in real …

Abstract 5 G is the fifth generation of cellular networks. It enables billions of connected
devices to gather and share information in real time; a key facilitator in Industrial Internet of …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Federated Learning (FL) has emerged as a de facto machine learning area and received
rapid increasing research interests from the community. However, catastrophic forgetting …