78 citations · 191 across the 15 of their papers we have counts for
16 papers
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…
Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning
Alfredo Petrella, Marco Miozzo, Paolo Dini
Traffic prediction represents one of the crucial tasks for smartly optimizing the mobile network. Recently, Artificial Intelligence (AI) has attracted attention to solve this probl…
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…
Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells
Dagnachew Azene Temesgene, Marco Miozzo, Deniz Gündüz +1
To meet the growing quest for enhanced network capacity, mobile network operators (MNOs) are deploying dense infrastructures of small cells. This, in turn, increases the power cons…
Recurrent Neural Networks for Handover Management in Next-Generation Self-Organized Networks
Zoraze Ali, Marco Miozzo, Lorenza Giupponi +3
In this paper, we discuss a handover management scheme for Next Generation Self-Organized Networks. We propose to extract experience from full protocol stack data, to make smart ha…
Dynamic Control of Functional Splits for Energy Harvesting Virtual Small Cells: a Distributed Reinforcement Learning Approach
Dagnachew Azene T., Marco Miozzo, Paolo Dini
In this paper, we propose a network scenario where the baseband processes of the virtual small cells powered solely by energy harvesters and batteries can be opportunistically exec…
Optimal Placement of Baseband Functions for Energy Harvesting Virtual Small Cells
Dagnachew A. Temesgene, Nicola Piovesan, Marco Miozzo +1
Flexible functional split in Cloud Radio Access Network (CRAN) greatly overcomes fronthaul capacity and latency challenges. In such architecture, part of the baseband processing is…
End-to-end 5G services via an SDN/NFV-based multi-tenant network and cloud testbed
Raul Muñoz, Josep Mangues-Bafalluy, Nikolaos Bartzoudis +11
5G has a main requirement of highly flexible, ultralow latency and ultra-high bandwidth virtualized infrastructure in order to deliver end-to-end services. This requirement can be…
Energy sustainable paradigms and methods for future mobile networks: A survey
Nicola Piovesan, Angel Fernandez Gambin, Marco Miozzo +2
In this survey, we discuss the role of energy in the design of future mobile networks and, in particular, we advocate and elaborate on the use of energy harvesting (EH) hardware as…
Simulating LTE and Wi-Fi Coexistence in Unlicensed Spectrum with ns-3
Lorenza Giupponi, Thomas Henderson, Biljana Bojovic +1
The use of unlicensed spectrum for future LTE systems raises concerns about its impact on co-located Wi-Fi networks. We report herein on extensions to a popular network simulator,…
SolarStat: Modeling Photovoltaic Sources through Stochastic Markov Processes
Marco Miozzo, Davide Zordan, Paolo Dini +1
In this paper, we present a methodology and a tool to derive simple but yet accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar source…