134 citations · 204 across the 14 of their papers we have counts for
35 papers
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…
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…