Federated learning (FL) becomes popular and has shown great potentials in training large-
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …

Federated learning has emerged as an effective paradigm to achieve privacy-preserving
collaborative learning among different parties. Compared to traditional centralized learning …

Federated Learning (FL) bridges the gap between collaborative machine learning and
preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is …

In current deep learning paradigms, local training or the Standalone framework tends to
result in overfitting and thus low utility. This problem can be addressed by Distributed or …

In federated learning (FL), data owners" share" their local data in a privacy preserving
manner in order to build a federated model, which in turn, can be used to generate revenues …

The current standalone deep learning framework tends to result in overfitting and low utility.
This problem can be addressed by either a centralized framework that deploys a central …

Recent advances in federated learning (FL) have brought large-scale collaborative machine
learning opportunities for massively distributed clients with performance and data privacy …

Federated learning is an emerging paradigm to conduct the machine learning
collaboratively but avoid the leakage of original data. Then, how to motivate the data owners …

In federated learning (FL), a federation distributedly trains a collective machine learning
model by leveraging privacy preserving technologies. However, FL participants need to …

Federated Learning is a framework that coordinates a large amount of workers to train a
shared model in a distributed manner, in which the training data are located on the workers' …