6 citations · 12 across the 5 of their papers we have counts for
6 papers
Decentralized Time Series Classification with ROCKET Features
Bruno Casella, Matthias Jakobs, Marco Aldinucci +1
Time series classification (TSC) is a critical task with applications in various domains, including healthcare, finance, and industrial monitoring. Due to privacy concerns and data…
A performance analysis of VM-based Trusted Execution Environments for Confidential Federated Learning
Bruno Casella
Federated Learning (FL) is a distributed machine learning approach that has emerged as an effective way to address recent privacy concerns. However, FL introduces the need for addi…
Benchmarking FedAvg and FedCurv for Image Classification Tasks
Bruno Casella, Roberto Esposito, Carlo Cavazzoni +1
Classic Machine Learning techniques require training on data available in a single data lake. However, aggregating data from different owners is not always convenient for different…
Experimenting with Normalization Layers in Federated Learning on non-IID scenarios
Bruno Casella, Roberto Esposito, Antonio Sciarappa +2
Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions. Federated Learning (FL) has been emerging as a metho…
Transfer Learning via Test-Time Neural Networks Aggregation
Bruno Casella, Alessio Barbaro Chisari, Sebastiano Battiato +1
It has been demonstrated that deep neural networks outperform traditional machine learning. However, deep networks lack generalisability, that is, they will not perform as good as…
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis
Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto +4
In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. However, recen…