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

Edge machine learning involves the development of learning algorithms at the network edge
to leverage massive distributed data and computation resources. Among others, the …

Abstract Federated Edge Learning (FEEL) is a promising distributed machine learning
paradigm in the era of edge intelligence, which supports to learn the knowledge in the …

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

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 envisioned as a promising paradigm to achieve privacy-
preserving distributed learning. However, it consumes excessive learning time due to the …

As one of the most popular and attractive frameworks for model training, federated edge
learning (FEEL) presents a new paradigm, which avoids direct data transmission by …

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 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 is a promising technology to deploy intelligence at the edge of
wireless networks in a privacy-preserving manner. Under such a setting, multiple clients …