33 citations · 118 across the 17 of their papers we have counts for
17 papers
Neural Network Compression by Approximate Differential Equivalence
Ravi Dhiman, Andrea Passarella, Mirco Tribastone +1
Neural network compression is commonly achieved by pruning parameters based on local importance scores, e.g., magnitude-based pruning. We propose a complementary approach that comp…
Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributions
Mirko Nardi, Lorenzo Valerio, Andrea Passarella
Federated Learning (FL) enables decentralized machine learning while preserving data privacy, making it ideal for sensitive applications where data cannot be shared. While FL has b…
FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher
Alessio Mora, Lorenzo Valerio, Paolo Bellavista +1
Federated Learning (FL) enables the collaborative training of machine learning models without requiring centralized collection of user data. To comply with the right to be forgotte…
Impact of network topology on the performance of Decentralized Federated Learning
Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio +2
Fully decentralized learning is gaining momentum for training AI models at the Internet's edge, addressing infrastructure challenges and privacy concerns. In a decentralized machin…
Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity
Lorenzo Valerio, Chiara Boldrini, Andrea Passarella +3
Federated Learning (FL) is a well-known framework for successfully performing a learning task in an edge computing scenario where the devices involved have limited resources and in…
Exploring the Impact of Disrupted Peer-to-Peer Communications on Fully Decentralized Learning in Disaster Scenarios
Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio +2
Fully decentralized learning enables the distribution of learning resources and decision-making capabilities across multiple user devices or nodes, and is rapidly gaining popularit…
The effect of network topologies on fully decentralized learning: a preliminary investigation
Luigi Palmieri, Lorenzo Valerio, Chiara Boldrini +1
In a decentralized machine learning system, data is typically partitioned among multiple devices or nodes, each of which trains a local model using its own data. These local models…
Anomaly Detection through Unsupervised Federated Learning
Mirko Nardi, Lorenzo Valerio, Andrea Passarella
Federated learning (FL) is proving to be one of the most promising paradigms for leveraging distributed resources, enabling a set of clients to collaboratively train a machine lear…
Design and evaluation of a cognitive approach for disseminating semantic knowledge and content in opportunistic networks
Matteo Mordacchini, Lorenzo Valerio, Marco Conti +1
In cyber-physical convergence scenarios information flows seamlessly between the physical and the cyber worlds. Here, users' mobile devices represent a natural bridge through which…
Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks
Saira Bano, Achilles Machumilane, Lorenzo Valerio +2
Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tu…
Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning
Lorenzo Valerio, Raffaele Bruno, Andrea Passarella
The widespread diffusion of mobile phones is triggering an exponential growth of mobile data traffic that is likely to cause, in the near future, considerable traffic overload issu…
A communication efficient distributed learning framework for smart environments
Lorenzo Valerio, Andrea Passarella, Marco Conti
Due to the pervasive diffusion of personal mobile and IoT devices, many ``smart environments'' (e.g., smart cities and smart factories) will be, among others, generators of huge am…
Energy efficient distributed analytics at the edge of the network for IoT environments
Lorenzo Valerio, Marco Conti, Andrea Passarella
Due to the pervasive diffusion of personal mobile and IoT devices, many "smart environments" (e.g., smart cities and smart factories) will be, generators of huge amounts of data. C…
Federated Feature Selection for Cyber-Physical Systems of Systems
Pietro Cassarà, Alberto Gotta, Lorenzo Valerio
Autonomous vehicles (AVs) generate a massive amount of multi-modal data that once collected and processed through Machine Learning algorithms, enable AI-based services at the Edge.…
Optimising cost vs accuracy of decentralised analytics in fog computing environments
Lorenzo Valerio, Andrea Passarella, Marco Conti
The exponential growth of devices and data at the edges of the Internet is rising scalability and privacy concerns on approaches based exclusively on remote cloud platforms. Data g…
Dynamic Hard Pruning of Neural Networks at the Edge of the Internet
Lorenzo Valerio, Franco Maria Nardini, Andrea Passarella +1
Neural Networks (NN), although successfully applied to several Artificial Intelligence tasks, are often unnecessarily over-parametrised. In edge/fog computing, this might make thei…
Application of a cognitive-inspired algorithm for detecting communities in mobility networks
Emanuele Massaro, Lorenzo Valerio, Andrea Guazzini +2
The emergence and the global adaptation of mobile devices has influenced human interactions at the individual, community, and social levels leading to the so called Cyber-Physical…