8 citations · 13 across the 13 of their papers we have counts for
8 papers · 1 filter
Distributed Learning in Markovian Restless Bandits over Interference Graphs for Stable Spectrum Sharing
Liad Lea Didi, Kobi Cohen
We study distributed learning for spectrum access and sharing among multiple cognitive communication entities, such as cells, subnetworks, or cognitive radio users (collectively re…
Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach
Dan Ben Ami, Kobi Cohen, Qing Zhao
Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchang…
Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach
Raz Paul, Kobi Cohen, Gil Kedar
We consider the problem of of multi-flow transmission in wireless networks, where data signals from different flows can interfere with each other due to mutual interference between…
Accelerated Gradient Descent Learning over Multiple Access Fading Channels
Raz Paul, Yuval Friedman, Kobi Cohen
We consider a distributed learning problem in a wireless network, consisting of N distributed edge devices and a parameter server (PS). The objective function is a sum of the edge…
Over-the-Air Federated Learning from Heterogeneous Data
Tomer Sery, Nir Shlezinger, Kobi Cohen +1
Federated learning (FL) is a framework for distributed learning of centralized models. In FL, a set of edge devices train a model using their local data, while repeatedly exchangin…
On Analog Gradient Descent Learning over Multiple Access Fading Channels
Tomer Sery, Kobi Cohen
We consider a distributed learning problem over multiple access channel (MAC) using a large wireless network. The computation is made by the network edge and is based on received d…