Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to
abide by the existing privacy regulations to preserve patients' anonymity. However, data is …

To enable the large scale and efficient deployment of Artificial Intelligence (AI), the
confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages …

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 is a machine learning scheme in which a shared prediction model can
be collaboratively learned by a number of distributed nodes using their locally stored data. It …

Federated learning (FL), as a distributed machine learning technology, allows large-scale
nodes to utilize local datasets for model training and sharing without revealing privacy …

Federated learning (FL) is a distributed machine learning approach that can achieve the
purpose of collaborative learning from a large amount of data that belong to different parties …

Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …

In traditional machine learning, the central server first collects the data owners' private data
together and then trains the model. However, people's concerns about data privacy …

Benefiting from the development of Federated Learning (FL) and distributed communication
systems, large-scale intelligent applications become possible. Distributed devices not only …