The REDRAW project investigates the exploitation of the federated learning computing paradigm to improve the technologies adopted for the monitoring, diagnosis and treatment management of specific health conditions, developing approaches more respectful of the constraints of privacy, confidentiality and cybersecurity, which are still largely absent from the market. REDRAW proposes the study and fine-tuning of dynamic cloud-edge deployment techniques, which exploits Federated Learning (FL) models, in three real-world contexts, to improve the technological features of existing solutions, while respecting the strategic and non-functional constraints that characterize the Italian and European scenarios .

REDRAW: fedeRatED leaRning for humAn Wellbeing

Aversa R.;Branco D.;Venticinque S.
2024

Abstract

The REDRAW project investigates the exploitation of the federated learning computing paradigm to improve the technologies adopted for the monitoring, diagnosis and treatment management of specific health conditions, developing approaches more respectful of the constraints of privacy, confidentiality and cybersecurity, which are still largely absent from the market. REDRAW proposes the study and fine-tuning of dynamic cloud-edge deployment techniques, which exploits Federated Learning (FL) models, in three real-world contexts, to improve the technological features of existing solutions, while respecting the strategic and non-functional constraints that characterize the Italian and European scenarios .
2024
Aversa, R.; Bochicchio, M.; Branco, D.; Magliulo, M.; Orlando, A.; Pristner, A.; Tramontano, A.; Schirinzi, E.; Siciliano, G.; Venticinque, S....espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/526408
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