Due to the privacy preserving capabilities and the low communication costs, federated
learning has emerged as an efficient technique for distributed deep learning/machine …

Federated Learning (FL) in mobile edge computing (MEC) systems has recently been
studied extensively. In ubiquitous environments, there are usually cross-edge devices that …

Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things and are
becoming an integral of our daily life. When tackling the evolving learning tasks in real …

Federated learning (FL) is a machine-learning setting, where multiple clients collaboratively
train a model under the coordination of a central server. The clients' raw data are locally …

Federated learning (FL) is a promising and privacy-preserving distributed learning method
that is widely deployed on edge devices. However, in practical applications, the data …

There has been a surge of interest in continual learning and federated learning, both of
which are important in deep neural networks in real-world scenarios. Yet little research has …

Learning over massive data stored in different locations is essential in many real-world
applications. However, sharing data is full of challenges due to the increasing demands of …

In federated learning, a strong global model is collaboratively learned by aggregating
clients' locally trained models. Although this precludes the need to access clients' data …

Federated learning is the state-of-the-art paradigm for training a learning model
collaboratively across multiple distributed devices while ensuring data privacy. Under this …

Federated Learning (FL) has gained significant attraction due to its ability to enable privacy-
preserving training over decentralized data. Current literature in FL mostly focuses on single …