A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866912075928829952 |
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| author | Santos, Diogo Reis Aillet, Albert Sund Boiano, Antonio Milasheuski, Usevalad Giusti, Lorenzo Di Gennaro, Marco Kianoush, Sanaz Barbieri, Luca Nicoli, Monica Carminati, Michele Redondi, Alessandro E. C. Savazzi, Stefano Serio, Luigi |
| author_facet | Santos, Diogo Reis Aillet, Albert Sund Boiano, Antonio Milasheuski, Usevalad Giusti, Lorenzo Di Gennaro, Marco Kianoush, Sanaz Barbieri, Luca Nicoli, Monica Carminati, Michele Redondi, Alessandro E. C. Savazzi, Stefano Serio, Luigi |
| contents | The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options, thereby reducing costs and improving patient outcomes. However, the sensitive nature of healthcare data presents significant challenges in terms of privacy and data ownership, hindering data availability and the development of robust algorithms. Federated Learning (FL) addresses these challenges by enabling collaborative training of ML models without the exchange of local data. This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes. This platform operates on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol. Considering the production readiness and data sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture, addressing potential threats and proposing mitigation strategies to enhance the platform's trustworthiness. The platform has been successfully tested in various operational environments using a publicly available dataset, highlighting its benefits and confirming its efficacy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13869 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers Santos, Diogo Reis Aillet, Albert Sund Boiano, Antonio Milasheuski, Usevalad Giusti, Lorenzo Di Gennaro, Marco Kianoush, Sanaz Barbieri, Luca Nicoli, Monica Carminati, Michele Redondi, Alessandro E. C. Savazzi, Stefano Serio, Luigi Computers and Society Distributed, Parallel, and Cluster Computing Machine Learning The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options, thereby reducing costs and improving patient outcomes. However, the sensitive nature of healthcare data presents significant challenges in terms of privacy and data ownership, hindering data availability and the development of robust algorithms. Federated Learning (FL) addresses these challenges by enabling collaborative training of ML models without the exchange of local data. This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes. This platform operates on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol. Considering the production readiness and data sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture, addressing potential threats and proposing mitigation strategies to enhance the platform's trustworthiness. The platform has been successfully tested in various operational environments using a publicly available dataset, highlighting its benefits and confirming its efficacy. |
| title | A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers |
| topic | Computers and Society Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2410.13869 |