Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

Smart cities demand resources for rich immersive sensing, ubiquitous communications,
powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of …

The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …

With the proliferation of distributed edge computing resources, the 6G mobile network will
evolve into a network for connected intelligence. Along this line, the proposal to incorporate …

The fifth generation (5G) and beyond wireless networks are envisioned to provide an
integrated communication and computing platform that will enable multipurpose and …

Recently, a large number of Low Earth Orbit (LEO) satellites have been launched and
deployed successfully in space by commercial companies, such as SpaceX. Due to …

The increasing complexity of deep neural networks poses significant barriers to
democratizing them to resource-limited edge devices. To address this challenge, split …

Motivated by ever-increasing computational resources at edge devices and increasing
privacy concerns, a new machine learning (ML) framework called federated learning (FL) …

As demands for multi-access edge computing (MEC) increase exponentially, resource
limitations at individual edge servers (ESs) will inevitably become bottlenecks. Most existing …

Split learning (SL) has emerged as a promising approach for model training without
revealing the raw data samples from the data owners. However, traditional SL inevitably …