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CERN Document Server 5 záznamov nájdených  Hľadanie trvalo 1.49 sekúnd. 
1.
A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers / Santos, Diogo Reis (CERN) ; Aillet, Albert Sund (CERN) ; Boiano, Antonio (Unlisted, IT) ; Milasheuski, Usevalad (Unlisted, IT ; CNR, Italy) ; Giusti, Lorenzo (CERN) ; Gennaro, Marco Di (Unlisted, IT) ; Kianoush, Sanaz (CNR, Italy) ; Barbieri, Luca (Unlisted, IT) ; Nicoli, Monica (CERN) ; Carminati, Michele (Unlisted, IT) et al.
The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. [...]
2024 - 7.
Úplný záznam
2.
A Close Look at the Communication Efficiency and the Energy Footprints of Robust Federated Learning in Industrial IoT / Barbieri, Luca (Milan, Polytech.) ; Kianoush, Sanaz (CNR, Milan) ; Nicoli, Monica (Milan, Polytech.) ; Serio, Luigi (CERN) ; Savazzi, Stefano (CNR,Milan)
Federated learning (FL) can be used to distribute machine learning (ML) tasks across edge and Internet of Things (IoT) devices with limited resources. FL provides an alternative and much more practical solution to classical artificial intelligence (AI), which requires moving large data volumes to energy-hungry data centers. [...]
2025 - 21 p. - Published in : IEEE Internet of Things Journal 12 (2025) 15130-15150 Fulltext: PDF; External link: Fulltext
Úplný záznam
3.
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification / Barbieri, Luca (Milan, Polytech.) ; Savazzi, Stefano ; Kianoush, Sanaz ; Nicoli, Monica (Milan, Polytech.) ; Serio, Luigi (CERN)
Federated Learning (FL) methods adopt efficient communication technologies to distribute machine learning tasks across edge devices, reducing the overhead in terms of data storage and computational complexity compared to centralized solutions. Rather than moving large data volumes from producers (sensors, machines) to energy-hungry data centers, raising environmental concerns due to resource demands, FL provides an alternative solution to mitigate the energy demands of several learning tasks while enabling new Artificial Intelligence of Things (AIoT) applications. [...]
arXiv:2310.08087.- 2023-11-06 - 6 p. - Published in : 10.1109/CAMAD59638.2023.10478391 Fulltext: PDF;
In : IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD 2023), Edinburgh, Scotland, 6-8 Nov 2023, pp.213-218
Úplný záznam
4.
A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications / Boiano, Antonio (Milan, Polytech.) ; Di Gennaro, Marco (Milan, Polytech.) ; Barbieri, Luca (Milan, Polytech.) ; Carminati, Michele (Milan, Polytech.) ; Nicoli, Monica (Milan, Polytech.) ; Redondi, Alessandro (Milan, Polytech.) ; Savazzi, Stefano (IFN, Rome) ; Aillet, Albert Sund (CERN) ; Santos, Diogo Reis (CERN) ; Serio, Luigi (CERN)
Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particularly in sensitive domains such as healthcare. [...]
arXiv:2404.11698. - 6 p.
Fulltext
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5.
Decentralized Federated Learning for Healthcare Networks: A Case Study on Tumor Segmentation / Camajori Tedeschini, Bernardo (Milan, Polytech.) ; Savazzi, Stefano (ISTP, Milan) ; Stoklasa, Roman (CERN) ; Barbieri, Luca (Milan, Polytech.) ; Stathopoulos, Ioannis (CERN) ; Nicoli, Monica (Milan, Polytech.) ; Serio, Luigi (CERN)
Smart healthcare relies on artificial intelligence (AI) functions for learning and analysis of patient data. Since large and diverse datasets for training of Machine Learning (ML) models can rarely be found in individual medical centers, classical centralized AI requires moving privacy-sensitive data from medical institutions to data centers that process the fused information. [...]
2022 - 16 p. - Published in : IEEE Access 10 (2022) 8693-8708 Fulltext: PDF;
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