Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT)
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …

In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

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 …

The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

The broad application of artificial intelligence techniques in medicine is currently hindered
by limited dataset availability for algorithm training and validation, due to the absence of …

In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

Federated learning has been widely studied and applied to various scenarios, such as
financial credit, medical identification, and so on. Under these settings, federated learning …

Federated learning (FL) is emerging as a new paradigm to train machine learning (ML)
models in distributed systems. Rather than sharing and disclosing the training data set with …

Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …

Quick global aggregation of effective distributed parameters is crucial to federated learning
(FL), which requires adequate bandwidth for parameters communication and sufficient user …