A decentralized and privacy-preserving framework for AI model training in healthcare: integrating federated learning and blockchain
Bahrami, Maziyar (A.A. 2023/2024) A decentralized and privacy-preserving framework for AI model training in healthcare: integrating federated learning and blockchain. Tesi di Laurea in Data privacy and security, Luiss Guido Carli, relatore Paolo Spagnoletti, pp. 95. [Master's Degree Thesis]
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Abstract/Index
Foundation technologies for secure decentralized learning in healthcare. Machine learning and deep learning. Federated learning. Blockchain technology. Blockchain technology in federated learning. Consensus mechanisms in blockchain for federated learning. Data management and ai in healthcare: trends, challenges, and innovations. Introduction to clinical research data management. AI and machine learning in healthcare. Synthetic data in healthcare: potential and challenges. Data privacy concerns in clinical research and IOMT with a focus on GDPR. Literature review of decentralized frameworks. Review of decentralized data management frameworks in healthcare. Synthetic data in federated learning frameworks. Existing federated learning blockchain-based frameworks for healthcare. Case studies in blockchain and federated learning in healthcare. Challenges and limitations in blockchain-based federated learning frameworks. Research design. Introduction to design science research (DSR). Framework architecture overview. Implementation details. Experimental setup and data simulation across nodes. Evaluation metrics. Evaluation of machine learning models. Blockchain performance and feasibility analysis. Feasibility for real-world deployment.
References
Bibliografia: pp. 85-89.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | Data privacy and security |
| Thesis Supervisor: | Spagnoletti, Paolo |
| Thesis Co-Supervisor: | Coppa, Emilio |
| Academic Year: | 2023/2024 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 15 Apr 2025 14:21 |
| Last Modified: | 15 Apr 2025 14:21 |
| URI: | https://tesi.luiss.it/id/eprint/41832 |
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