33 citations · 118 across the 17 of their papers we have counts for
11 papers · 1 filter
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…
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…
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.…
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…