With the advent of connected devices with computation and storage capabilities, it’s now possible for you to run machine learning workflows entirely on device.
Alex Ingerman examines federated learning and other technologies that enable devices to collaboratively and securely learn ML models while retaining all data locally. Federated learning improves upon traditional, fully centralized approaches by reducing the costs and risks related to sensitive data handling, working better in bandwidth- and power-constrained environments, and providing a straightforward, effective mechanism for personalization at scale. It puts users back in control of their data while still enabling developers to build intelligent applications that leverage insights from that data. Federated learning is already used at scale by Google.
Alex Ingerman leads the product management team at Google Research, focusing on federated learning and other privacy-preserving technologies for machine learning. He joined Google in 2016 after working on products including ML-as-a-service platform for developers, web-scale search, content recommendation system, and immersive data-exploration environments. Alex holds a BS in computer science and an MS in medical engineering.
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