1 citations · 1 across the 2 of their papers we have counts for
4 papers
UAV-Aided Decentralized Learning over Mesh Networks
Matteo Zecchin, David Gesbert, Marios Kountouris
Decentralized learning empowers wireless network devices to collaboratively train a machine learning (ML) model relying solely on device-to-device (D2D) communication. It is known…
Asynchronous Decentralized Learning over Unreliable Wireless Networks
Eunjeong Jeong, Matteo Zecchin, Marios Kountouris
Decentralized learning enables edge users to collaboratively train models by exchanging information via device-to-device communication, yet prior works have been limited to wireles…
User-Centric Federated Learning
Mohamad Mestoukirdi, Matteo Zecchin, David Gesbert +2
Data heterogeneity across participating devices poses one of the main challenges in federated learning as it has been shown to greatly hamper its convergence time and generalizatio…
Team Deep Mixture of Experts for Distributed Power Control
Matteo Zecchin, David Gesbert, Marios Kountouris
In the context of wireless networking, it was recently shown that multiple DNNs can be jointly trained to offer a desired collaborative behaviour capable of coping with a broad ran…