With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …

Massive data collection in modern systems has paved the way for data-driven machine
learning, a promising technique for creating reliable and robust statistical models. By …

In this paper, we investigate a distributed machine learning approach for a multiuser mobile
edge computing (MEC) network in a cognitive eavesdropping environment, where multiple …

In this paper, we study a relay-assisted federated edge learning (FEEL) network under
latency and bandwidth constraints. In this network, users collaboratively train a global model …

New technology is needed to meet the latency and bandwidth issues present in cloud
computing architecture specially to support the currency of 5G networks. Accordingly, mobile …

Recently, federated learning (FL) has emerged as a promising distributed machine learning
(ML) technology, owing to the advancing computational and sensing capacities of end-user …

With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …

Federated learning is a promising tool in the Internet-of-Things (IoT) domain for training a
machine learning model in a decentralized manner. Specifically, the data owners (eg, IoT …

The massive number of Internet of Things (IoT) devices connected to the Internet is
continuously increasing. The operations of these devices rely on consuming huge amounts …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …