Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Sensor-cloud originates from extensive recent applications of wireless sensor networks and
cloud computing. To draw a roadmap of the current research activities of the sensor-cloud …

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to
cooperatively train a deep learning model, without the need to share their local data. It is a …

Federated learning (FL) is becoming a popular paradigm for collaborative learning over
distributed, private data sets owned by nontrusting entities. FL has seen successful …

In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …

Federated learning (FL) is a promising paradigm that enables collaboratively learning a
shared model across massive clients while kee** the training data locally. However, for …

The rapid growth of the Internet of Vehicles (IoV) paradigm sparks the generation of large
volumes of distributed data at vehicles, which can be harnessed to build models for …

Distributed learning algorithms aim to leverage distributed and diverse data stored at users'
devices to learn a global phenomena by performing training amongst participating devices …