Ep. 81: Federated Learning with DLT - Mathieu Galtier (CPO Owkin)

Mathieu Galtier, PhD – Neuroscience and computer science (applied mathematics) background. Chief Product Officer of Owkin which was founded in 2016 and has quickly emerged as a leader in bringing AI and ML technologies to the healthcare industry. We’ll be talking about how blockchain technology and federated learning systems are creating amazing new opportunities in medical research and development. LinkedIn https://www.linkedin.com/in/mgaltier/ Guest Twitter: https://twitter.com/m_galtier Research: https://www.researchgate.net/profile/Mathieu-Galtier MELLODDY: https://www.imi.europa.eu/projects-results/project-factsheets/melloddy Owkin • Origins of Owkin and the healthcare interest - life science company - research oriented - really want to cure cancer o Medical data & Clinical predictive models o Connect platform for Federated Learning • Owkin Tech stack overview o Data prep o Infra: Kubernetes o Application layer: Substra (python, Hyperledger fabric) o Algorithms: any python ML frameworks (pytorch, tensorflow etc) • open source foundation to host Substra framework for distributed computation ML for Healthcare • Why is ML relevant for Healthcare? o Precision medicine: customization of treatment to individual patients based on their medical data. o• Clinical Trials Research challenges and opportunities oFederated Learning for Healthcare • What is Federated Learning? o Model training across distributed datasets. Data is never shared. (opposition centralized computation vs traveling models) o Can you describe how federated learning can solve some of our privacy and security concerns with personal data? o HealthChain project o Melloddy project • What is the HealthChain project's main objective? • MELLODDY consortium - Owkin, NVIDIA, and King’s College London - Have you reached your goals? What's new with this collaboration? • Academic collaboration → HealthChain o Industrial coopetition → Melloddy • BLockchain in Federated Learning • Why is a blockchain relevant for Federated Learning? What is in the block