Internet of Things (IoT) generates large amounts of data at the network edge. Machine
learning models are often built on these data, to enable the detection, classification, and …

Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices.
IoT devices with intelligence require the use of effective machine learning paradigms …

The advancement of the Internet of Things (IoT) brings new opportunities for collecting real-
time data and deploying machine learning models. Nonetheless, an individual IoT device …

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 new distributed machine learning paradigm that many clients (eg,
mobile devices or organizations) collaboratively train a model under the orchestration of a …

To leverage massive distributed data and computation resources, machine learning in the
network edge is considered to be a promising technique, especially for large-scale model …

Machine learning (ML), and deep learning (DL) in particular, play a vital role in providing
smart services to the industry. These techniques, however, suffer from privacy and security …

Federated learning (FL) is emerging as a new paradigm to train machine learning (ML)
models in distributed systems. Rather than sharing and disclosing the training data set with …

Emerging technologies and applications have generated large amounts of data at the
network edge. Due to bandwidth, storage, and privacy concerns, it is often impractical to …

When applying machine learning techniques to the Internet of things, aggregating massive
amount of data seriously reduce the system efficiency. To tackle this challenge, a distributed …