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Autor:
Terrail, J.O.D. (Jean Ogier Du), Ayed, S-S. (Samy-Safwan), Cyffers, E. (Edwige), Grimberg, F. (Felix), He, C. (Chaoyang), Loeb, R. (Regis), Mangold, P. (Paul), Marchand, T. (Tanguy), Marfoq, O. (Othmane), Mushtaq, E. (Erum), Muzellec, B. (Boris), Philippenko, C. (Constantin), Silva, S. (Santiago), Teleńczuk, M. (Maria), Albarqouni, S. (Shadi), Avestimehr, S. (Salman), Bellet, A. (Aurélien), Dieuleveut, A. (Aymeric), Jaggi, M. (Martin), Karimireddy, S.P. (Sai Praneeth), Lorenzi, M. (Marco), Neglia, G. (Giovanni), Tommasi, M. (Marc), Andreux, M. (Mathieu)
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-silo FL setting corresponds to the case of few ($2$--$50$) reliable cli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4198::59e1d08b8cb778173e081790ebb952ff
http://hdl.handle.net/20.500.12210/80036
http://hdl.handle.net/20.500.12210/80036
