Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| author | Cremonesi, Francesco Vesin, Marc Cansiz, Sergen Bouillard, Yannick Balelli, Irene Innocenti, Lucia Silva, Santiago Ayed, Samy-Safwan Taiello, Riccardo Kameni, Laetita Vidal, Richard Orlhac, Fanny Nioche, Christophe Lapel, Nathan Houis, Bastien Modzelewski, Romain Humbert, Olivier Önen, Melek Lorenzi, Marco |
| author_facet | Cremonesi, Francesco Vesin, Marc Cansiz, Sergen Bouillard, Yannick Balelli, Irene Innocenti, Lucia Silva, Santiago Ayed, Samy-Safwan Taiello, Riccardo Kameni, Laetita Vidal, Richard Orlhac, Fanny Nioche, Christophe Lapel, Nathan Houis, Bastien Modzelewski, Romain Humbert, Olivier Önen, Melek Lorenzi, Marco |
| contents | The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security. While today several FL libraries are proposed to data scientists and users, most of these frameworks are not designed to find seamless application in medical use-cases, due to the specific challenges and requirements of working with medical data and hospital infrastructures. Moreover, governance, design principles, and security assumptions of these frameworks are generally not clearly illustrated, thus preventing the adoption in sensitive applications. Motivated by the current technological landscape of FL in healthcare, in this document we present Fed-BioMed: a research and development initiative aiming at translating federated learning (FL) into real-world medical research applications. We describe our design space, targeted users, domain constraints, and how these factors affect our current and future software architecture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_12012 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications Cremonesi, Francesco Vesin, Marc Cansiz, Sergen Bouillard, Yannick Balelli, Irene Innocenti, Lucia Silva, Santiago Ayed, Samy-Safwan Taiello, Riccardo Kameni, Laetita Vidal, Richard Orlhac, Fanny Nioche, Christophe Lapel, Nathan Houis, Bastien Modzelewski, Romain Humbert, Olivier Önen, Melek Lorenzi, Marco Machine Learning Distributed, Parallel, and Cluster Computing The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security. While today several FL libraries are proposed to data scientists and users, most of these frameworks are not designed to find seamless application in medical use-cases, due to the specific challenges and requirements of working with medical data and hospital infrastructures. Moreover, governance, design principles, and security assumptions of these frameworks are generally not clearly illustrated, thus preventing the adoption in sensitive applications. Motivated by the current technological landscape of FL in healthcare, in this document we present Fed-BioMed: a research and development initiative aiming at translating federated learning (FL) into real-world medical research applications. We describe our design space, targeted users, domain constraints, and how these factors affect our current and future software architecture. |
| title | Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2304.12012 |