Fantastyc: Blockchain-based Federated Learning Made Secure and Practical
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Boitier, William Del Pozzo, Antonella García-Pérez, Álvaro Gazut, Stephane Jobic, Pierre Lemaire, Alexis Mahe, Erwan Mayoue, Aurelien Perion, Maxence Rezende, Tuanir Franca Singh, Deepika Tucci-Piergiovanni, Sara |
| author_facet | Boitier, William Del Pozzo, Antonella García-Pérez, Álvaro Gazut, Stephane Jobic, Pierre Lemaire, Alexis Mahe, Erwan Mayoue, Aurelien Perion, Maxence Rezende, Tuanir Franca Singh, Deepika Tucci-Piergiovanni, Sara |
| contents | Federated Learning is a decentralized framework that enables multiple clients to collaboratively train a machine learning model under the orchestration of a central server without sharing their local data. The centrality of this framework represents a point of failure which is addressed in literature by blockchain-based federated learning approaches. While ensuring a fully-decentralized solution with traceability, such approaches still face several challenges about integrity, confidentiality and scalability to be practically deployed. In this paper, we propose Fantastyc, a solution designed to address these challenges that have been never met together in the state of the art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03608 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fantastyc: Blockchain-based Federated Learning Made Secure and Practical Boitier, William Del Pozzo, Antonella García-Pérez, Álvaro Gazut, Stephane Jobic, Pierre Lemaire, Alexis Mahe, Erwan Mayoue, Aurelien Perion, Maxence Rezende, Tuanir Franca Singh, Deepika Tucci-Piergiovanni, Sara Cryptography and Security Distributed, Parallel, and Cluster Computing Federated Learning is a decentralized framework that enables multiple clients to collaboratively train a machine learning model under the orchestration of a central server without sharing their local data. The centrality of this framework represents a point of failure which is addressed in literature by blockchain-based federated learning approaches. While ensuring a fully-decentralized solution with traceability, such approaches still face several challenges about integrity, confidentiality and scalability to be practically deployed. In this paper, we propose Fantastyc, a solution designed to address these challenges that have been never met together in the state of the art. |
| title | Fantastyc: Blockchain-based Federated Learning Made Secure and Practical |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2406.03608 |