26847@AAAI

#1 Industry-Scale Orchestrated Federated Learning for Drug Discovery [PDF] [Copy] [Kimi]

Authors: 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°831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.