78 citations · 92 across the 11 of their papers we have counts for
6 papers · 1 filter
Degradation of Feature Space in Continual Learning
Chiara Lanza, Roberto Pereira, Marco Miozzo +2
Centralized training is the standard paradigm in deep learning, enabling models to learn from a unified dataset in a single location. In such setup, isotropic feature distributions…
Self-Supervised Learning at the Edge: The Cost of Labeling
Roberto Pereira, Fernanda Famá, Asal Rangrazi +3
Contrastive learning (CL) has recently emerged as an alternative to traditional supervised machine learning solutions by enabling rich representations from unstructured and unlabel…
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…
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting
Nikolaos Pavlidis, Vasileios Perifanis, Selim F. Yilmaz +6
The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular t…
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…
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…