Federated Learning (FL) serves privacy-preserving collaborative learning among multiple
isolated parties, while retaining their privacy data locally. Cross-device and cross-silo FL …

Training task in classical machine learning models, such as deep neural networks, is
generally implemented at a remote cloud center for centralized learning, which is typically …

Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed
learning with a network of clients (ie, edge devices). However, the data distribution among …

Federated learning (FL) and split learning (SL) are two popular distributed machine learning
approaches. Both follow a model-to-data scenario; clients train and test machine learning …

Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet
wireless channels bring challenges for model training, in which channel randomness …

Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

In the traditional cloud architecture, data needs to be uploaded to the cloud for processing,
bringing delays in transmission and response. Edge network emerges as the times require …

Federated learning (FL) has become de facto framework for collaborative learning among
edge devices with privacy concern. The core of the FL strategy is the use of stochastic …

Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing. To fully utilize the widely distributed data, we concentrate on a wireless …

With the proliferation of mobile computing and Internet of Things (IoT), massive mobile and
IoT devices are connected to the Internet. These devices are generating a huge amount of …