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 …

Federated learning (FL) provides a privacy-preserving approach to realizing networked
intelligence. However, the performance of FL is often constrained by the limited …

Federated edge learning (FL), as an emerging distributed machine learning paradigm,
allows a mass of edge devices to collaboratively train a global model while preserving …

Federated learning is a new artificial intelligence technology with which an edge server can
orchestrate with multiple end users to train a global model collaboratively. Under this setting …

Federated learning (FL) allows model training from local data by edge devices while
preserving data privacy. However, the learning accuracy decreases due to the heterogeneity …

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

As a typical distributed learning paradigm, federated learning has enabled network edge
intelligence by making full use of the local data and the computing resources at edge …

Federated learning (FL) is a swiftly evolving field within machine learning for collaboratively
training models at the network edge in a privacy-preserving fashion, without training 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 …