Fantastyc: Blockchain-Based Federated Learning Made Secure and Practical
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.
Authors
- Aurélien Mayoue (ORCID: https://orcid.org/0000-0002-7330-3024)
- Sara Tucci-Piergiovanni (ORCID: https://orcid.org/0000-0001-9738-9021)
- Álvaro García-Pérez (ORCID: https://orcid.org/0000-0002-9558-6037)
- Erwan Mahe (ORCID: https://orcid.org/0000-0002-5322-4337)
- Tuanir França Rezende
- Antonella Del Pozzo (ORCID: https://orcid.org/0000-0003-0913-2141)
- William Boitier
- Stephane Gazut
- Pierre Jobic (ORCID: https://orcid.org/0009-0009-2720-1324)
- Alexis Lemaire
- Maxence Perion
- Deepika Singh
Institutions
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Laboratoire d'Intégration des Systèmes et des Technologies (FR)
Publication Details
- Published
- 2024-09-30
- DOI
- https://doi.org/10.1109/srds64841.2024.00033
- Citations
- 8
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 2.78