Federated Learning (FL) is a distributed learning paradigm that can learn a global or
personalized model from decentralized datasets on edge devices. However, in the computer …

Federated Learning (FL) is a machine learning paradigm where many local nodes
collaboratively train a central model while keeping the training data decentralized. This is …

In this paper, we identify a new phenomenon called activation-divergence which occurs in
Federated Learning (FL) due to data heterogeneity (ie, data being non-IID) across multiple …

Federated learning has been extensively explored in privacy-preserving medical image
analysis. However, the domain shift widely existed in real-world scenarios still greatly limits …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can bring separate data sources together and build machine learning …

Federated learning (FL), training deep models from decentralized data without privacy
leakage, has shown great potential in medical image computing recently. However …

Driven by the deep learning (DL) revolution, artificial intelligence (AI) has become a
fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic …

Federated learning has emerged as a promising distributed machine learning paradigm to
preserve data privacy. One of the fundamental challenges of federated learning is that data …

Federated learning (FL) enables multiple clients to jointly train high-performance deep
learning models while maintaining the training data locally. However, it is challenging to …

of a centralized model. Li et al (5) subsequently recognized the need for privacy-preserving
methods and implemented differential privacy techniques to reduce the possible risk of …