@mastersthesis{EURECOM+7289,
  author = {Cremonesi, Francesco and  Vesin, Marc and  Cansiz, Sergen and  Bouillard, Yannick and  Balelli, Irene and  Innocenti, Lucia and  Silva, Santiago and  Ayed, Samy-Safwan and  Taiello, Riccardo and  Kameni, Laetita and  Vidal, Richard and  Orlhac, Fanny and  Nioche, Christophe and  Lapel, Nathan and  Houis, Bastien and  Modzelewski, Romain and  Humbert, Olivier and  Önen, Melek and  Lorenzi, Marco},
  title = {Fed-BioMed: Open, transparent and trusted federated learning for real-world healthcare applications},
  year = {2023},
  note = {© EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Submitted to ArXiV, 24 April 2023 and is available at :},
}