The widely deployed devices in Internet of Things (IoT) have opened up a large amount of
IoT data. Recently, federated learning emerges as a promising solution aiming to protect …

Federated learning has been popularly studied with people's increasing awareness of
privacy protection. It solves the problem of privacy leakage by its ability that allows many …

Depending on large-scale devices, the Internet of Things (IoT) provides massive data
support for resource sharing and intelligent decision, but privacy risks also increase. As a …

With the massive deployment of the Internet of Things (IoT) devices, many data analysis
applications emerge for the large amount of data accumulated by IoT. Federated learning …

Based on the vulnerability of federated learning (FL) to inference attacks and the high
computation overhead, lack of label protection and degraded model performance occurred …

Federated Learning (FL) is a distributed Machine Learning (ML) framework, which has been
considered as a reliable approach to preserve privacy for the widely used Internet of Things …

Federated learning (FL) is a distributed machine learning paradigm in the Internet of Things
(IoT), which allows multiple devices to collaboratively train models without leaking local …

With the rapid development of the Internet of Things (IoT), federated learning (FL) has been
widely used to obtain insights from collected data while preserving data privacy. Differential …

In the Internet of things (IoT) networks, largescale IoT devices are connected to the Internet
to collect users' data. As a distributed machine learning paradigm, federated learning (FL) …

Abstract Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a
reputation for not only building Machine Learning (ML) models that rely on distributed …