In the medical domain, gathering all data and training a global supervised model is very
difficult due to scattered data from different hospitals and security and privacy concerns. In …

Hospitals and research institutions may not be willing to share their collected medical data
due to privacy concerns, transmission cost, and the intrinsic value of the data. Federated …

Quantities and diversities of datasets are vital to model training in a variety of medical image
diagnosis applications. However, there are the following problems in real scenes: the …

Access to sufficient annotated data is a common challenge in training deep neural networks
on medical images. As annotating data is expensive and time-consuming, it is difficult for an …

Medical data sharing across institutes is crucial to large-scale multi-center studies and the
development of real-world AI applications but suffers from serious privacy issues. A …

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 …

Deep learning-based algorithms have led to tremendous progress over the last years, but
they face a bottleneck as their optimal development highly relies on access to large …

One-shot federated learning (FL) has emerged as a promising solution in scenarios where
multiple communication rounds are not practical. Notably, as feature distributions in medical …

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 …