Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …

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 …

Abstract The rise of Federated Learning (FL) is bringing machine learning to edge
computing by utilizing data scattered across edge devices. However, the heterogeneity of …

Aviation Internet of Things (AIoT) data sharing can create tremendous value for participants.
With the development of AIoT and intelligent civil aviation, data security and privacy …

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 provides a privacy-preserving manner to collaboratively train models on
data distributed over multiple local clients via the coordination of a global server. In this …

Federated deep learning is the method of choice for performing deep learning in
environments where data sharing is not allowed due to privacy/security issues. However, all …

At present, there is a pressing need for data scientists and academic researchers to devise
advanced machine learning and artificial intelligence-driven systems that can effectively …

This paper focuses on the latest research and innovations in fundamental next-generation
multiple access (NGMA) techniques and the coexistence with other key technologies for the …

TinyML/DL is a new subfield of ML that allows for the deployment of ML algorithms on low-
power devices to process their own data. The lack of resources restricts the aforementioned …