Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for
distributed machine learning using training data distributed at edge devices. This framework …

Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient
solution to enable distributed machine learning over edge devices by using their data locally …

Federated edge learning (FEEL) is a popular framework for model training at an edge server
using data distributed at edge devices (eg, smart-phones and sensors) without …

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient solution for
privacy-preserving distributed learning over wireless networks. Air-FEEL allows" one-shot" …

Federated Edge Learning (FEEL) is a distributed machine learning technique where each
device contributes to training a global inference model by independently performing local …

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 learning (FL) is an emerging distributed training scheme where edge devices
collaboratively train a model by uploading model updates instead of private data. To …

To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …

Over-the-air federated edge learning (Air-FEEL) has emerged as a promising solution to
support edge artificial intelligence (AI) in future, beyond 5G (B5G) and 6G networks. In Air …

To mitigate the multi-access latency in federated edge learning, an efficient broadband
analog transmission scheme has been recently proposed, featuring the aggregation of …