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 …

Federated edge learning (FEEL) emerges as a privacy-preserving paradigm to effectively
train deep learning models from the distributed data in 6G networks. Nevertheless, the …

Federated Learning (FL) has been proposed as an appealing approach to handle data
privacy issue of mobile devices compared to conventional machine learning at the remote …

Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …

We envision a mobile edge computing (MEC) framework for machine learning (ML)
technologies, which leverages distributed client data and computation resources for training …

FEderated Edge Learning (FEEL) has emerged as a leading technique for privacy-
preserving distributed training in wireless edge networks, where edge devices …

Edge machine learning involves the deployment of learning algorithms at the network edge
to leverage massive distributed data and computation resources to train artificial intelligence …

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

Federated edge learning (FEEL) has emerged as an effective approach to reduce the large
communication latency in Cloud-based machine learning solutions, while preserving data …