Federated learning (FL) is a promising new technology that allows machine learning (ML)
models to be trained locally on edge devices while preserving the privacy of the devices' …

Recently, federated learning (FL) has received tremendous attention in both academia and
industry, in which decentralized clients collaboratively complete model training by …

As a promising distributed technology, federated learning (FL) has been widely used in
vehicular networks involving large amounts of IoT-enabled sensor data, which derives …

Federated learning (FL) enables distributed model training from local data collected by
users. In distributed systems with constrained resources and potentially high dynamics, eg …

Federated learning (FL) is a promising framework for privacy-preserving collaborative
learning, where model training tasks are distributed to clients and only the model updates …

Federated Learning (FL) is an efficient distributed machine learning paradigm that employs
private datasets in a privacy-preserving manner. The main challenges of FL are that end …

Federated learning-enabled mobile edge computing implements privacy-preserving
collaborative machine learning of complex models. However, mobile end devices have high …

When workers are heterogeneous in computing and transmission capabilities, the global
efficiency of federated learning suffers from the straggler issue, ie, the slowest worker drags …

We consider the problem of training a given machine learning model by decentralized
parallel stochastic gradient descent over training data distributed across multiple nodes …

The Fifth Generation (5G) mobile systems support many kinds of intelligent cloud services.
The upcoming Sixth Generation (6G) mobile systems aim to provide customized cloud …