134 citations · 397 across the 39 of their papers we have counts for
50 papers
XR Offloading Across Multiple Time Scales: The Roles of Power, Temperature, and Energy
Francesco Malandrino, Olga Chukhno, Alessandro Catania +2
Extended reality (XR) devices, commonly known as wearables, must handle significant computational loads under tight latency constraints. To meet these demands, they rely on a combi…
Eavesdropping with Intelligent Reflective Surfaces: Near-Optimal Configuration Cycling
Francesco Malandrino, Alessandro Nordio, Carla Fabiana Chiasserini
Intelligent reflecting surfaces (IRSs) have several prominent advantages, including improving the level of wireless communication security and privacy. In this work, we focus on th…
Dependable Distributed Training of Compressed Machine Learning Models
Francesco Malandrino, Giuseppe Di Giacomo, Marco Levorato +1
The existing work on the distributed training of machine learning (ML) models has consistently overlooked the distribution of the achieved learning quality, focusing instead on its…
Human-Centric Decision-Making in Cell-Less 6G Networks
Emma Chiaramello, Carla Fabiana Chiasserini, Francesco Malandrino +3
In next-generation networks, cells will be replaced by a collection of points-of-access (PoAs), with overlapping coverage areas and/or different technologies. Along with a promise…
Unexpectedly Useful: Convergence Bounds And Real-World Distributed Learning
Francesco Malandrino, Carla Fabiana Chiasserini
Convergence bounds are one of the main tools to obtain information on the performance of a distributed machine learning task, before running the task itself. In this work, we perfo…
Matching DNN Compression and Cooperative Training with Resources and Data Availability
Francesco Malandrino, Giuseppe Di Giacomo, Armin Karamzade +2
To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the req…
Virtual Service Embedding with Time-Varying Load and Provable Guarantees
Gil Einziger, Gabriel Scalosub, Carla Fabiana Chiasserini +1
Deploying services efficiently while satisfying their quality requirements is a major challenge in network slicing. Effective solutions place instances of the services' virtual net…
Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN
Jorge Martín-Pérez, Nuria Molner, Francesco Malandrino +3
Open Radio Access Network (O-RAN) is an emerging paradigm, whereby virtualized network infrastructure elements from different vendors communicate via open, standardized interfaces.…
Performance and EMF Exposure Trade-offs in Human-centric Cell-free Networks
Francesco Malandrino, Emma Chiaramello, Marta Parazzini +1
In cell-free wireless networks, multiple connectivity options and technologies are available to serve each user. Traditionally, such options are ranked and selected solely based on…
Efficient Distributed DNNs in the Mobile-edge-cloud Continuum
Francesco Malandrino, Carla Fabiana Chiasserini, Giuseppe Di Giacomo
In the mobile-edge-cloud continuum, a plethora of heterogeneous data sources and computation-capable nodes are available. Such nodes can cooperate to perform a distributed learning…
Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost
Francesco Malandrino, Carla Fabiana Chiasserini
Traditionally, distributed machine learning takes the guise of (i) different nodes training the same model (as in federated learning), or (ii) one model being split among multiple…
Eavesdropping with Intelligent Reflective Surfaces: Threats and Defense Strategies
Francesco Malandrino, Alessandro Nordio, Carla Fabiana Chiasserini
Intelligent reflecting surfaces (IRSs) have several prominent advantages, including improving the level of wireless communications security and privacy. In this work, we focus on t…
Edge-powered Assisted Driving For Connected Cars
Francesco Malandrino, Carla Fabiana Chiasserini, Gian Michele dell'Aera
Assisted driving for connected cars is one of the main applications that 5G-and-beyond networks shall support. In this work, we propose an assisted driving system leveraging the sy…
Towards Node Liability in Federated Learning: Computational Cost and Network Overhead
Francesco Malandrino, Carla Fabiana Chiasserini
Many machine learning (ML) techniques suffer from the drawback that their output (e.g., a classification decision) is not clearly and intuitively connected to their input (e.g., an…
Optimization of IRS-aided Sub-THz Communications under practical design constraints
Alberto Tarable, Francesco Malandrino, Laura Dossi +3
We consider the optimization of a smart radio environment where meta-surfaces are employed to improve the performance of multiuser wireless networks working at sub-THz frequencies.…
Dynamic VNF Placement, Resource Allocation and Traffic Routing in 5G
Morteza Golkarifard, Carla Fabiana Chiasserini, Francesco Malandrino +1
5G networks are going to support a variety of vertical services, with a diverse set of key performance indicators (KPIs), by using enabling technologies such as software-defined ne…
Federated Learning at the Network Edge: When Not All Nodes are Created Equal
Francesco Malandrino, Carla Fabiana Chiasserini
Under the federated learning paradigm, a set of nodes can cooperatively train a machine learning model with the help of a centralized server. Such a server is also tasked with assi…
Network Support for High-performance Distributed Machine Learning
Francesco Malandrino, Carla Fabiana Chiasserini, Nuria Molner +1
The traditional approach to distributed machine learning is to adapt learning algorithms to the network, e.g., reducing updates to curb overhead. Networks based on intelligent edge…
Scheduling of Emergency Tasks for Multiservice UAVs in Post-Disaster Scenarios
Cristina Rottondi, Francesco Malandrino, Andrea Bianco +2
Single-task UAVs are increasingly being employed to carry out surveillance, parcel delivery, communication support, and other specific tasks. When the geographical area of operatio…
An Edge-powered Approach to Assisted Driving
Francesco Malandrino, Carla Fabiana Chiasserini, Gian Michele Dell'Aera
Automotive services for connected vehicles are one of the main fields of application for new-generation mobile networks as well as for the edge computing paradigm. In this paper, w…
Graph-based Model for Beam Management in Mmwave Vehicular Networks
Zana Limani Fazliu, Carla Fabiana Chiasserini, Francesco Malandrino +1
Mmwave bands are being widely touted as a very promising option for future 5G networks, especially in enabling such networks to meet highly demanding rate requirements. Accordingly…
Mmwave Beam Management in Urban Vehicular Networks
Zana Limani, Francesco Malandrino, Carla Fabiana Chiasserini +1
Millimeter-wave (mmwave) communication represents a potential solution to capacity shortage in vehicular networks. However, effective beam alignment between senders and receivers r…
Active Learning-based Classification in Automated Connected Vehicles
Alaa Awad Abdellatif, Carla Fabiana Chiasserini, Francesco Malandrino
Machine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. A key challen…
Characterizing Delay and Control Traffic of the Cellular MME with IoT Support
Christian Vitale, Carla Fabiana Chiasserini, Francesco Malandrino +1
One of the main use cases for advanced cellular networks is represented by massive Internet-of-things (MIoT), i.e., an enormous number of IoT devices that transmit data toward the…