Abstract: Federated learning in consumer scenarios has garnered a lot of interest. However, its application in large enterprises brings to bear additional needs and guarantees. In this talk, I will highlight key drivers for federated learning in enterprises, illustrate representative uses cases, and summarize the requirements for a platform that can support it. I will then present the newly released IBM Federated Learning framework (git, white paper) and show how it can be used and extended by researchers. Finally, I will highlight recent advances in federated learning and privacy from IBM Research.

Biography: Rania Khalaf is the Director of AI Platforms and Runtimes at IBM Research where she leads teams pushing the envelope in AI platforms to make creating AI models and applications easy, fast, and safe for data scientists and developers. Her multi-disciplinary teams tackle key problems at the intersection of core AI, distributed systems, human computer interaction and cloud computing. Prior to this role, Rania was Director of Cloud Platform, Programming Models and Runtimes. Rania serves as a Judge for the MIT Solve AI for Humanity Prize, on the Leadership Challenge Group for MIT Solve's Learning for Girils and Women Challenge and on the Advisory Board of the Hariri Institute for Computing at Boston University. She has received several Outstanding Technical Innovation awards for major impact to the field of computer science and was a finalist for the 2019 MassTLC CTO of the Year award.