The Sched app allows you to build your schedule but is not a substitute for your event registration. You must be registered for KubeCon + CloudNativeCon North America 2021 - Los Angeles, CA + Virtual and add this Co-Located event to your registration to participate in these sessions. If you have not registered but would like to join us, please go to the event registration page to purchase a registration.
Please note: This schedule is automatically displayed in Pacific Standard Time (PST), UTC -7. To see the schedule in your preferred timezone, please select from the drop-down menu to the right, above "Filter by Date." The schedule is subject to change.
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.
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.
