13 papers
Federated Learning with Downlink Device Selection
Mohammad Mohammadi Amiri, Sanjeev R. Kulkarni, H. Vincent Poor
We study federated edge learning, where a global model is trained collaboratively using privacy-sensitive data at the edge of a wireless network. A parameter server (PS) keeps trac…
Blind Federated Edge Learning
Mohammad Mohammadi Amiri, Tolga M. Duman, Deniz Gunduz +2
We study federated edge learning (FEEL), where wireless edge devices, each with its own dataset, learn a global model collaboratively with the help of a wireless access point actin…
Communicate to Learn at the Edge
Deniz Gunduz, David Burth Kurka, Mikolaj Jankowski +3
Bringing the success of modern machine learning (ML) techniques to mobile devices can enable many new services and businesses, but also poses significant technical and research cha…
Convergence of Federated Learning over a Noisy Downlink
Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni +1
We study federated learning (FL), where power-limited wireless devices utilize their local datasets to collaboratively train a global model with the help of a remote parameter serv…
Federated Learning With Quantized Global Model Updates
Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni +1
We study federated learning (FL), which enables mobile devices to utilize their local datasets to collaboratively train a global model with the help of a central server, while keep…
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems
Yo-Seb Jeon, Mohammad Mohammadi Amiri, Jun Li +1
Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices, each with its own local training data set. I…