The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …

Neural networks trained on distilled data often produce over-confident output and require
correction by calibration methods. Existing calibration methods such as temperature scaling …

Federated learning (FL) is an emerging distributed machine learning method that empowers
in-situ model training on decentralized edge devices. However, multiple simultaneous FL …

Federated learning (FL) is a hot collaborative training framework via aggregating model
parameters of decentralized local clients. However, most FL methods unreasonably assume …

Existing federated learning methods have effectively dealt with decentralized learning in
scenarios involving data privacy and non-IID data. However, in real-world situations, each …

Existing federated learning methods have effectively addressed decentralized learning in
scenarios involving data privacy and non-IID data. However, in real-world situations, each …

Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …

Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …

Catastrophic forgetting, which means a rapid forgetting of learned representations while
learning new data/samples, is one of the main problems of deep neural networks. In this …

Federated Continual Learning (FCL) has emerged as a promising paradigm that combines
Federated Learning (FL) and Continual Learning (CL). To achieve good model accuracy …