5 citations · 6 across the 6 of their papers we have counts for
6 papers
Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theory
Alessandro Buratto, Elia Guerra, Marco Miozzo +2
The Internet of Things requires intelligent decision making in many scenarios. To this end, resources available at the individual nodes for sensing or computing, or both, can be le…
Energy-Aware Decentralized Learning with Intermittent Model Training
Akash Dhasade, Paolo Dini, Elia Guerra +5
Decentralized learning (DL) offers a powerful framework where nodes collaboratively train models without sharing raw data and without the coordination of a central server. In the i…
The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies
Francesc Wilhelmi, Nima Afraz, Elia Guerra +1
Blockchain promises to enhance distributed machine learning (ML) approaches such as federated learning (FL) by providing further decentralization, security, immutability, and trust…
Towards Energy-Aware Federated Traffic Prediction for Cellular Networks
Vasileios Perifanis, Nikolaos Pavlidis, Selim F. Yilmaz +6
Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network…
The Cost of Training Machine Learning Models over Distributed Data Sources
Elia Guerra, Francesc Wilhelmi, Marco Miozzo +1
Federated learning is one of the most appealing alternatives to the standard centralized learning paradigm, allowing a heterogeneous set of devices to train a machine learning mode…
On the Decentralization of Blockchain-enabled Asynchronous Federated Learning
Francesc Wilhelmi, Elia Guerra, Paolo Dini
Federated learning (FL), thanks in part to the emergence of the edge computing paradigm, is expected to enable true real-time applications in production environments. However, its…