We study federated machine learning (ML) at the wireless edge, where power-and
bandwidth-limited wireless devices with local datasets carry out distributed stochastic …

We study distributed machine learning at the wireless edge, where limited power devices
(workers) with local datasets implement distributed stochastic gradient descent (DSGD) over …

We study federated machine learning at the wireless network edge, where limited power
wireless devices, each with its own dataset, build a joint model with the help of a remote …

Edge machine learning involves the deployment of learning algorithms at the wireless
network edge so as to leverage massive mobile data for enabling intelligent applications …

We consider a many-to-one wireless architecture for federated learning at the network edge,
where multiple edge devices collaboratively train a model using local data. The unreliable …

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over
siloed data centers is motivating renewed interest in the collaborative training of a shared …

Federated learning (FL) is a promising technique that enables many edge devices to train a
machine learning model collaboratively in wireless networks. By exploiting the superposition …

Federated learning has emerged as a popular technique for distributing machine learning
(ML) model training across the wireless edge. In this paper, we propose two timescale …

Federated Learning (FL), an emerging paradigm for fast intelligent acquisition at the network
edge, enables joint training of a machine learning model over distributed data sets and …

The deployment of federated learning in a wireless network, called federated edge learning
(FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model …