Publications of Riccardo Taiello

1. Riccardo Taiello. Privacy-preserving machine learning for large-scale collaborative healthcare data analysis. Theses, Université Côte d'Azur, September 2024. Keyword(s): Security and privacy, Privacy enhancing technologies, Federate learning, Sécurité et confidentialité, Technologies de renforcement de la confidentialité, Apprentissage fédéré. [bibtex-entry]

1. Riccardo Taiello, Melek Önen, Francesco Capano, Olivier Humbert, and Marco Lorenzi. Privacy preserving image registration. Medical Image Analysis, 94, May 2024. [bibtex-entry]

1. Riccardo Taiello, Melek Önen, Clémentine Gritti, and Marco Lorenzi. Let Them Drop: Scalable and Efficient Federated Learning Solutions Agnostic to Stragglers. In ARES 2024 - 19th International Conference on Availability, Reliability and Security, number 13 of ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security, Vienna, Austria, pages 1-12, July 2024. ACM. Keyword(s): Attribute-Based Encryption, Time-based Access Control, Direct Revocation, Internet of Things, preserving protocols, Security protocols, Synchronous and Asynchronous Federated Learning, Secure Aggregation, Role authorities, Time authority, Sensors Actuator Proxy. [bibtex-entry]



2. Riccardo Taiello, Melek Önen, Olivier Humbert, and Marco Lorenzi. Privacy Preserving Image Registration. In MICCAI 2022 - Medical Image Computing and Computer Assisted Intervention, Singapore, Singapore, September 2022. Keyword(s): Image Registration, Privacy enhancing technologies, Trustworthiness. [bibtex-entry]

1. Francesco Cremonesi, Marc Vesin, Sergen Cansiz, Yannick Bouillard, Irene Balelli, Lucia Innocenti, Santiago Silva, Samy-Safwan Ayed, Riccardo Taiello, Laetita Kameni, Richard Vidal, Fanny Orlhac, Christophe Nioche, Nathan Lapel, Bastien Houis, Romain Modzelewski, Olivier Humbert, Melek Önen, and Marco Lorenzi. Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. Note: Working paper or preprint, April 2023. Keyword(s): Machine learning, Biomedical Application, Healthcare, Federated Learning Framework. [bibtex-entry]

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