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Presentation: Federated Learning: Rewards & Challenges of Distributed Private ML

Track: Deep Learning in Practice

Location: Cyril Magnin II

Duration: 4:20pm - 5:00pm

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Abstract

Federated Learning is a recent paradigm for machine learning that addresses user privacy concerns while also opening up orders of magnitude larger datasets for machine learning tasks by leaving data on user devices and pushing computation to the edge. In this talk, we will cover the basic concepts underlying the federated approach, the advantages it brings, as well as the machine learning and engineering challenges associated with constructing federated solutions. We will also focus on the use of federated learning for products in health and medicine, where successful implementations must pay special attention to privacy. 

Speaker: Eric Tramel

Federated Learning R&D Lead @OWKIN

Eric is the head of Owkin’s Federated Learning Research group, where he heads a team of researchers and engineers to study and build applications of federated and privacy-aware machine learning techniques to medical data. Before joining Owkin, Eric served in postdoc roles at Inria and École Normale Supérieure, studying the interface between statistical physics and machine learning & information theory. He earned his Ph.D. and B.S. degrees in Computer Engineering from Mississippi State University.

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