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October 12, 2021
Los Angeles, California + Virtual
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IMPORTANT NOTE: Timing of sessions and room locations are subject to change through Monday, September 13 due to schedule changes that will be made as speakers finalize whether speaking in person or virtually.
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Tuesday, October 12 • 9:30am - 10:00am
What, Why and How of Federated Machine Learning – Implementation Using FATE, KubeFATE, and FATE-Operator for Kubeflow - Neeraj Arora & Layne Peng, VMware

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Federated Machine Learning (FML) is an emerging technology that makes it possible to extract insights from widely dispersed data sources. It may also be used with privacy enhancing measures to reveal insights without revealing the underlying data. In doing so, it gives global organizations an opportunity to utilize worldwide data while adhering to regulations for different geographic regions, allows insights to be developed without moving large amounts of data to a central location, and in a multi-party scenario allows each party to control their individual decision to participate. This talk will introduce FML discussing its basic principles, its manifestations, high-level use-cases, and tie these in with the technology being developed. To move from theory to practice, we will demonstrate how FML can be implemented on Kubernetes using FATE, KubeFATE, and integrated with Kubeflow using the FATE-Operator. We’ll also discuss new features being implemented into FATE for realistic use-cases. A similar presentation was delivered at CNOS Virtual Summit China in July 2020 and focused on the integration of FATE into Kubeflow. The current presentation will cover technical ground instead on the challenges and opportunities of FML for real-world use-cases encountered since.

Speakers
avatar for Layne Peng

Layne Peng

Staff II Technologist, VMware
avatar for Neeraj Arora

Neeraj Arora

Application Platforms Architect, VMware



Tuesday October 12, 2021 9:30am - 10:00am PDT
Room 502 AB + Online