Industry-Scale Orchestrated Federated Learning for Drug Discovery

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
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引用次数: 6

Abstract

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.
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针对药物发现的工业规模精心策划的联合学习
为了将联合学习应用于药物发现,我们在欧洲创新药物倡议(IMI)项目MELLODDY(资助号831472)的背景下开发了一个新的平台,该项目由10家制药公司、学术研究实验室、大型工业公司和初创公司组成。MELLODDY平台是第一个工业规模的平台,可以在不共享单个合作伙伴的机密数据集的情况下创建药物发现的全球联合模型。通过在每次训练迭代之后以加密、安全的方式聚合所有贡献伙伴的梯度,在平台上训练联邦模型。该平台部署在Amazon Web Services (AWS)多账户架构上,在私有子网中运行Kubernetes集群。在组织上,不同合作伙伴的角色被编码为平台上不同的权利和权限,并以分散的方式进行管理。MELLODDY平台产生了新的科学发现,这些发现在一篇配套论文中有描述。
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