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eess.SP2024
Feasibility Analysis of Federated Neural Networks for Explainable Detection of Atrial Fibrillation
Diogo Reis Santos, Andrea Protani, Lorenzo Giusti +3
Early detection of atrial fibrillation (AFib) is challenging due to its asymptomatic and paroxysmal nature. However, advances in deep learning algorithms and the vast collection of…
eess.SP2024
Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals
Andrea Protani, Lorenzo Giusti, Chiara Iacovelli +9
After an acute stroke, accurately estimating stroke severity is crucial for healthcare professionals to effectively manage patient's treatment. Graph theory methods have shown that…
eess.SP2024
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification
Luca Barbieri, Stefano Savazzi, Sanaz Kianoush +2
Federated Learning (FL) methods adopt efficient communication technologies to distribute machine learning tasks across edge devices, reducing the overhead in terms of data storage…