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 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 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), as an emerging distributed machine learning paradigm, allows a
mass of edge devices to collaboratively train a global model while preserving privacy. In this …

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 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 (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 …

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 …

The stringent requirements for low-latency and privacy of the emerging high-stake
applications with intelligent devices such as drones and smart vehicles make the cloud …

Federated learning (FL) is a privacy-preserving machine learning setting that enables many
devices to jointly train a shared global model without the need to reveal their data to a …