Federated edge learning (FEEL) has drawn much attention as a privacy-preserving
distributed learning framework for mobile edge networks. In this work, we investigate a novel …

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 edge learning (FEEL) is gaining increasing attention due to its characteristics of
privacy protection, low latency, and low communication overhead. However, it still faces …

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 …

Federated learning (FL) is a new paradigm for privacy-preserving learning. This is
particularly appealing in the mobile edge network (MEN), in which devices collectively train …

Federated edge learning (FEEL) is a novel paradigm that enables privacy-preserving and
distributed machine learning on end devices. However, FEEL faces challenges from …

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 edge learning (FEEL) is a popular distributed learning framework for privacy-
preserving at the edge, in which densely distributed edge devices periodically exchange …

Federated edge learning (FEEL) is a promising distributed learning technique for next-
generation wireless networks. FEEL preserves the user's privacy, reduces the …

To fully exploit the enormous data generated by the devices in edge computing, edge
federated learning (EFL) is envisioned as a promising solution. The distributed collaborative …