83 citations · 83 across the 4 of their papers we have counts for
4 papers
FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
Lingyu Qiu, Daniela Annunziata, Stefano Izzo +2
With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning. Althoug…
Towards One-shot Federated Learning: Advances, Challenges, and Future Directions
Flora Amato, Lingyu Qiu, Mohammad Tanveer +3
One-shot FL enables collaborative training in a single round, eliminating the need for iterative communication, making it particularly suitable for use in resource-constrained and…
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
Salvatore Cuomo, Vincenzo Schiano di Cola, Fabio Giampaolo +3
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itsel…
Deep Learning for the Approximation of a Shape Functional
F. Calabrò, S. Cuomo, F. Giampaolo +4
Artificial Neuronal Networks are models widely used for many scientific tasks. One of the well-known field of application is the approximation of high-dimensional problems via Deep…