Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

The future of machine learning is decentralized

Alex Ingerman (Google)
11:00am11:40am Thursday, March 28, 2019
Secondary topics:  Security and Privacy, Storage

Who is this presentation for?

  • ML practitioners, data engineers, and product managers interested in the intersection of machine learning and privacy



Prerequisite knowledge

  • Familiarity with machine learning concepts and workflows
  • A working knowledge of modern ML frameworks, such as TensorFlow

What you'll learn

  • Explore federated learning: How it works, how it differs from traditional, centralized machine learning, abnd which use cases it fits best
  • Get a practical demo in TensorFlow


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 their 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.

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. Along the way, you’ll also explore ecure aggregation: using cryptography to simulate a trusted aggregator, adding another layer of security and privacy protections in the federated learning environment.

Photo of Alex Ingerman

Alex Ingerman


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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