Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929508270997504 |
|---|---|
| author | Bonesana, Claudio Malpetti, Daniele Mitrović, Sandra Mangili, Francesca Azzimonti, Laura |
| author_facet | Bonesana, Claudio Malpetti, Daniele Mitrović, Sandra Mangili, Francesca Azzimonti, Laura |
| contents | We present Flotta, a Federated Learning framework designed to train machine learning models on sensitive data distributed across a multi-party consortium conducting research in contexts requiring high levels of security, such as the biomedical field. Flotta is a Python package, inspired in several aspects by Apache Spark, which provides both flexibility and security and allows conducting research using solely machines internal to the consortium. In this paper, we describe the main components of the framework together with a practical use case to illustrate the framework's capabilities and highlight its security, flexibility and user-friendliness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13473 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework Bonesana, Claudio Malpetti, Daniele Mitrović, Sandra Mangili, Francesca Azzimonti, Laura Machine Learning We present Flotta, a Federated Learning framework designed to train machine learning models on sensitive data distributed across a multi-party consortium conducting research in contexts requiring high levels of security, such as the biomedical field. Flotta is a Python package, inspired in several aspects by Apache Spark, which provides both flexibility and security and allows conducting research using solely machines internal to the consortium. In this paper, we describe the main components of the framework together with a practical use case to illustrate the framework's capabilities and highlight its security, flexibility and user-friendliness. |
| title | Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.13473 |