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 is a product manager at Google AI, focusing on federated learning and other privacy-preserving technologies. His mission is to enable all ML practitioners to protect their users’ privacy by default. Previously, Alex worked on ML-as-a-service platforms for developers, web-scale search, content recommendation systems, and immersive data exploration and visualization. Alex lives in Seattle, where as a frequent bike and occasional kayak commuter, he has fully embraced the rain. Alex holds a BS in computer science and an MS in medical engineering.
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