no code implementations • 17 Oct 2022 • Martijn Oldenhof, Gergely Ács, Balázs Pejó, Ansgar Schuffenhauer, Nicholas Holway, Noé Sturm, Arne Dieckmann, Oliver Fortmeier, Eric Boniface, Clément Mayer, Arnaud Gohier, Peter Schmidtke, Ritsuya Niwayama, Dieter Kopecky, Lewis Mervin, Prakash Chandra Rathi, Lukas Friedrich, András Formanek, Peter Antal, Jordon Rahaman, Adam Zalewski, Wouter Heyndrickx, Ezron Oluoch, Manuel Stößel, Michal Vančo, David Endico, Fabien Gelus, Thaïs de Boisfossé, Adrien Darbier, Ashley Nicollet, Matthieu Blottière, Maria Telenczuk, Van Tien Nguyen, Thibaud Martinez, Camille Boillet, Kelvin Moutet, Alexandre Picosson, Aurélien Gasser, Inal Djafar, Antoine Simon, Ádám Arany, Jaak Simm, Yves Moreau, Ola Engkvist, Hugo Ceulemans, Camille Marini, Mathieu Galtier
To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups.
no code implementations • 23 Jun 2021 • Balázs Pejó, Gergely Biczók
Most governments employ a set of quasi-standard measures to fight COVID-19 including wearing masks, social distancing, virus testing, contact tracing, and vaccination.
1 code implementation • 13 Jul 2020 • Balázs Pejó, Gergely Biczók
In this paper, we focus on the quality of individual training datasets and show that such quality information could be inferred and attributed to specific participants even when secure aggregation is applied.