@book{EURECOM+8109,
  author = {Cremonesi, Francesco and  Vesin, Marc and  Cansiz, Sergen and  Bouillard, Yannick and  Balelli, Irene and  Innocenti, Lucia and  Taiello, Riccardo and  Silva, Santiago and  Ayed, Samy-Safwan and  Önen, Melek and  et al.},
  title = {Fed-BioMed: Open, transparent and trusted federated learning for real-world healthcare applications},
  year = {2025},
  series = {Chapter book in \"Federated Learning Systems\", Studies in Computational Intelligence 832, Springer, 2nd ed.\&\#13;\&\#10;},
  note = {©  Springer. Personal use of this material is permitted. The definitive version of this paper was published in Chapter book in \"Federated Learning Systems\", Studies in Computational Intelligence 832, Springer, 2nd ed.\&\#13;\&\#10; and is available at : https://doi.org/10.1007/978-3-031-78841-3\_2},
  editor = {Springer},
}