Federated learning introduction and examples with TensorFlow Federated
Who is this presentation for?
- ML practitioners, data engineers, and product managers interested in the intersection of machine learning and privacy
With the advent of connected devices with computation and storage capabilities, it’s now possible to run machine learning workflows entirely on device. Federated learning is an approach for training ML models across a fleet of participating devices without collecting the data in a central location. Federated learning improves upon the 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 also 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, where it powers predictive input capabilities in the Android keyboard (Gboard), on-device search for Pixel phones, and other experiences. The recent release of the open source TensorFlow Federated project enables all ML developers to experiment with federated technologies. Alex Ingerman offers an overview of federated learning, compares traditional and federated ML workflows, and explores the current and upcoming use cases for decentralized machine learning with examples from Google’s deployment of this technology. You’ll have the opportunity to get your hands dirty in a hands-on introduction to TensorFlow Federated as Alex shows you how it can be used to train federated models on decentralized datasets.
- A working knowledge of supervised machine learning and deep learning
- Familiarity with TensorFlow
What you'll learn
- Understand what federated learning is, how it compares to traditional ML, and when it's appropriate to use
- Learn about machine learning and privacy—if they're even compatible—what the different considerations are, and what's meant by privacy, anyway
- Discover how to get stared with TensorFlow Federated and experiment with federated learning on your own data
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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Alex, can you please post the presentation?