38 citations · 87 across the 21 of their papers we have counts for
26 papers
Interaction-Aware Gaussian Weighting for Clustered Federated Learning
Alessandro Licciardi, Davide Leo, Eros Fanì +2
Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy. However, conventional FL struggles with data heterogeneity and class imbalance…
Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning
Debora Caldarola, Pietro Cagnasso, Barbara Caputo +1
Federated learning (FL) enables collaborative model training with privacy preservation. Data heterogeneity across edge devices (clients) can cause models to converge to sharp minim…
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
Eros Fanì, Raffaello Camoriano, Barbara Caputo +1
Federated Learning (FL) methods often struggle in highly statistically heterogeneous settings. Indeed, non-IID data distributions cause client drift and biased local solutions, par…
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum
Riccardo Zaccone, Sai Praneeth Karimireddy, Carlo Masone +1
Federated Learning (FL) has emerged as the state-of-the-art approach for learning from decentralized data in privacy-constrained scenarios.However, system and statistical challenge…
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Driving
Eros Fanì, Marco Ciccone, Barbara Caputo
We propose FedDrive v2, an extension of the Federated Learning benchmark for Semantic Segmentation in Autonomous Driving. While the first version aims at studying the effect of dom…
Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning
Debora Caldarola, Barbara Caputo, Marco Ciccone
Federated Learning (FL) aims to learn a global model from distributed users while protecting their privacy. However, when data are distributed heterogeneously the learning process…
Schedule-Robust Online Continual Learning
Ruohan Wang, Marco Ciccone, Giulia Luise +3
A continual learning (CL) algorithm learns from a non-stationary data stream. The non-stationarity is modeled by some schedule that determines how data is presented over time. Most…
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning
Donald Shenaj, Eros Fanì, Marco Toldo +6
Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the co…
A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving
Luca Carminati, Federico Cacciamani, Marco Ciccone +1
\emph{Ex ante} correlation is becoming the mainstream approach for \emph{sequential adversarial team games}, where a team of players faces another team in a zero-sum game. It is kn…
Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead
Niccolò Cavagnero, Fernando Dos Santos, Marco Ciccone +3
Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving. However,…
Improving Generalization in Federated Learning by Seeking Flat Minima
Debora Caldarola, Barbara Caputo, Marco Ciccone
Models trained in federated settings often suffer from degraded performances and fail at generalizing, especially when facing heterogeneous scenarios. In this work, we investigate…
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving
Lidia Fantauzzo, Eros Fanì, Debora Caldarola +4
Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. Howe…
Public Information Representation for Adversarial Team Games
Luca Carminati, Federico Cacciamani, Marco Ciccone +1
The peculiarity of adversarial team games resides in the asymmetric information available to the team members during the play, which makes the equilibrium computation problem hard…
Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients
Riccardo Zaccone, Andrea Rizzardi, Debora Caldarola +2
Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. T…
Incremental Learning in Semantic Segmentation from Image Labels
Fabio Cermelli, Dario Fontanel, Antonio Tavera +2
Although existing semantic segmentation approaches achieve impressive results, they still struggle to update their models incrementally as new categories are uncovered. Furthermore…
Cluster-driven Graph Federated Learning over Multiple Domains
Debora Caldarola, Massimiliano Mancini, Fabio Galasso +3
Federated Learning (FL) deals with learning a central model (i.e. the server) in privacy-constrained scenarios, where data are stored on multiple devices (i.e. the clients). The ce…
DA4Event: towards bridging the Sim-to-Real Gap for Event Cameras using Domain Adaptation
Mirco Planamente, Chiara Plizzari, Marco Cannici +5
Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events". The innovative way they acquire data presents seve…
Multi-Agent Coordination in Adversarial Environments through Signal Mediated Strategies
Federico Cacciamani, Andrea Celli, Marco Ciccone +1
Many real-world scenarios involve teams of agents that have to coordinate their actions to reach a shared goal. We focus on the setting in which a team of agents faces an opponent…
A Differentiable Recurrent Surface for Asynchronous Event-Based Data
Marco Cannici, Marco Ciccone, Andrea Romanoni +1
Dynamic Vision Sensors (DVSs) asynchronously stream events in correspondence of pixels subject to brightness changes. Differently from classic vision devices, they produce a sparse…
Coordination in Adversarial Sequential Team Games via Multi-Agent Deep Reinforcement Learning
Andrea Celli, Marco Ciccone, Raffaele Bongo +1
Many real-world applications involve teams of agents that have to coordinate their actions to reach a common goal against potential adversaries. This paper focuses on zero-sum game…
Attention Mechanisms for Object Recognition with Event-Based Cameras
Marco Cannici, Marco Ciccone, Andrea Romanoni +1
Event-based cameras are neuromorphic sensors capable of efficiently encoding visual information in the form of sparse sequences of events. Being biologically inspired, they are com…
ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation
Francesco Lattari, Marco Ciccone, Matteo Matteucci +2
We introduce ReConvNet, a recurrent convolutional architecture for semi-supervised video object segmentation that is able to fast adapt its features to focus on any specific object…
Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras
Marco Cannici, Marco Ciccone, Andrea Romanoni +1
Event-based cameras, also known as neuromorphic cameras, are bioinspired sensors able to perceive changes in the scene at high frequency with low power consumption. Becoming availa…
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
Marco Ciccone, Marco Gallieri, Jonathan Masci +2
This paper introduces Non-Autonomous Input-Output Stable Network(NAIS-Net), a very deep architecture where each stacked processing block is derived from a time-invariant non-autono…