655 citations · 1.6k across the 31 of their papers we have counts for
34 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…
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…
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge
Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni +1
We study federated learning (FL) at the wireless edge, where power-limited devices with local datasets collaboratively train a joint model with the help of a remote parameter serve…
Machine Learning Methods for Attack Detection in the Smart Grid
Mete Ozay, Inaki Esnaola, Fatos T. Yarman Vural +2
Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online sett…