655 citations · 1.6k across the 31 of their papers we have counts for
16 papers · 1 filter
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…
Sparse Attack Construction and State Estimation in the Smart Grid: Centralized and Distributed Models
Mete Ozay, Inaki Esnaola, Fatos T. Yarman Vural +2
New methods that exploit sparse structures arising in smart grid networks are proposed for the state estimation problem when data injection attacks are present. First, construction…