no code implementations • 9 May 2024 • Luca Barbieri, Stefano Savazzi, Monica Nicoli
Bayesian Federated Learning (FL) has been recently introduced to provide well-calibrated Machine Learning (ML) models quantifying the uncertainty of their predictions.
no code implementations • 29 Apr 2024 • Usevalad Milasheuski. Luca Barbieri, Bernardo Camajori Tedeschini, Monica Nicoli, Stefano Savazzi
Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information.
no code implementations • 17 Apr 2024 • Antonio Boiano, Marco Di Gennaro, Luca Barbieri, Michele Carminati, Monica Nicoli, Alessandro Redondi, Stefano Savazzi, Albert Sund Aillet, Diogo Reis Santos, Luigi Serio
This paper provides an overview of the TRUSTroke FL network infrastructure.
no code implementations • 26 Feb 2024 • Luca Barbieri, Bernardo Camajori Tedeschini, Mattia Brambilla, Monica Nicoli
In line with this trend, this paper proposes a novel data-driven cooperative sensing framework, termed Cooperative LiDAR Sensing with Message Passing Neural Network (CLS-MPNN), where spatially-distributed vehicles collaborate in perceiving the environment via LiDAR sensors.
no code implementations • 17 Nov 2023 • Lorenzo Italiano, Bernardo Camajori Tedeschini, Mattia Brambilla, Huiping Huang, Monica Nicoli, Henk Wymeersch
The widespread adoption of the fifth generation (5G) of cellular networks has brought new opportunities for the development of localization-based services.
no code implementations • 12 Oct 2023 • Luca Barbieri, Stefano Savazzi, Sanaz Kianoush, Monica Nicoli, Luigi Serio
Federated Learning (FL) methods adopt efficient communication technologies to distribute machine learning tasks across edge devices, reducing the overhead in terms of data storage and computational complexity compared to centralized solutions.
no code implementations • 19 Oct 2022 • Luca Barbieri, Osvaldo Simeone, Monica Nicoli
Bayesian Federated Learning (FL) offers a principled framework to account for the uncertainty caused by limitations in the data available at the nodes implementing collaborative training.
no code implementations • 25 Jan 2022 • Marco Manzoni, Dario Tagliaferri, Marco Rizzi, Stefano Tebaldini, Andrea Virgilio Monti-Guarnieri, Claudio Maria Prati, Monica Nicoli, Ivan Russo, Sergi Duque, Christian Mazzucco, Umberto Spagnolini
With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment.
no code implementations • 28 Oct 2021 • Marco Rizzi, Marco Manzoni, Stefano Tebaldini, Andrea Virgilio Monti-Guarnieri, Claudio Maria Prati, Dario Tagliaferri, Monica Nicoli, Ivan Russo, Christian Mazzucco, Simón Tejero Alfageme, Umberto Spagnolini
Automotive synthetic aperture radar (SAR) systems are rapidly emerging as a candidate technological solution to enable a high-resolution environment mapping for autonomous driving.
no code implementations • 28 Oct 2021 • Marco Manzoni, Marco Rizzi, Stefano Tebaldini, Andrea Virgilio Monti-Guarnieri, Claudio Maria Prati, Dario Tagliaferri, Monica Nicoli, Ivan Russo, Christian Mazzucco, Sergi Duque Biarge, Umberto Spagnolini
This paper deals with the analysis, estimation, and compensation of trajectory errors in automotive-based Synthetic Aperture Radar (SAR) systems.
no code implementations • 5 Aug 2021 • Mattia Brambilla, Domenico Gaglione, Giovanni Soldi, Rico Mendrzik, Gabriele Ferri, Kevin D. LePage, Monica Nicoli, Peter Willett, Paolo Braca, Moe Z. Win
This paper addresses the problem of multitarget tracking using a network of sensing agents with unknown positions.
no code implementations • 10 Jun 2021 • Marouan Mizmizi, Francesco Linsalata, Mattia Brambilla, Filippo Morandi, Kai Dong, Maurizio Magarini, Monica Nicoli, Majid Nasiri Khormuji, Peng Wang, Renaud Alexandre Pitaval, Umberto Spagnolini
The ever-increasing demand for intelligent, automated, and connected mobility solutions pushes for the development of an innovative sixth Generation (6G) of cellular networks.
no code implementations • 8 Apr 2021 • Dario Tagliaferri, Mattia Brambilla, Monica Nicoli, Umberto Spagnolini
Ultra-reliable low-latency Vehicle-to-Everything (V2X) communications are needed to meet the extreme requirements of enhanced driving applications.
2 code implementations • 9 Jan 2021 • Stefano Savazzi, Monica Nicoli, Mehdi Bennis, Sanaz Kianoush, Luca Barbieri
Next-generation autonomous and networked industrial systems (i. e., robots, vehicles, drones) have driven advances in ultra-reliable, low latency communications (URLLC) and computing.
1 code implementation • 27 Dec 2019 • Stefano Savazzi, Monica Nicoli, Vittorio Rampa
Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems.