With the ever-growing complexity of models in the field of remote sensing (RS), there is an
increasing demand for solutions that balance model accuracy with computational efficiency …

Federated learning emerges as a solution to the dilemma of data silos while safeguarding
data privacy, particularly relevant in the consumer electronics sector where user data privacy …

Federated Learning (FL) enables the training of Deep Learning models without centrally
collecting possibly sensitive raw data. This paves the way for stronger privacy guarantees …

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where
data analysis and decision-making can be performed in real-time and close to data sources …

The growing interest in intelligent services and privacy protection for mobile devices has
given rise to the widespread application of federated learning in Multi-access Edge …

Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices
without compromising their privacy. As computing tasks are increasingly performed by a …

The increasing demand for intelligent services and privacy protection of mobile and Internet
of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL), in …

Federated Learning (FL) is a popular distributed machine learning paradigm that enables
devices to work together to train a centralized model without transmitting raw data. However …

Federated fault diagnosis has attracted increasing attention in industrial cloud–edge
collaboration scenarios, where a ubiquitous assumption is that client models have the same …

Federated learning (FL) aims to collaboratively train a shared model across multiple clients
without transmitting their local data. Data heterogeneity is a critical challenge in realistic FL …