The future of machine learning is decentralized

This will be presented in English.

Alex Ingerman (Google)
13:1013:50 Thursday, June 20, 2019

必要预备知识 (Prerequisite Knowledge)

  • Familiarity with machine learning (ML) applications
  • General knowledge of distributed ML approaches
  • Familiarity with the TensorFlow framework (useful but not required)

您将学到什么 (What you'll learn)

  • Learn what federated learning is, how it's different from the traditional ML approach, and how it interacts with other privacy-preserving technologies
  • Understand how Google uses large-scale real-world uses of federated learning in Google products, including end-to-end systems overview
  • Discover how to try out federated learning on your own data and TensorFlow models and evaluate the results

描述 (Description)

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.

Photo of Alex Ingerman

Alex Ingerman


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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