6 citations · 10 across the 3 of their papers we have counts for
3 papers
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…