Presented By
O’REILLY + INTEL AI

English中文
PUT AI TO WORK
June 18-21, 2019
Beijing, CN

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)

Attendees should be familiar with machine learning applications, and have some exposure to distributed machine learning approaches. Familiarity with the TensorFlow framework will be helpful for the demo and do-it-yourself part of the talk, but not required.

您将学到什么 (What you'll learn)

* (Overview) What is federated learning, and how is it different from the traditional ML approach * (Overview) How federated learning differs from / interacts with other privacy-preserving technologies * (Case studies) Large-scale real world uses of federated learning in Google products, including end-to-end systems overview. * (Skill) 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 is now possible to run machine learning workflows entirely on-device. This talk will introduce federated learning and other technologies that enable devices to collaboratively and securely learn ML models, while retaining all data locally. 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 – come to this talk to hear how!

Photo of Alex Ingerman

Alex Ingerman

Google

Alex Ingerman leads the product management team at Google Research, focusing on federated learning and other privacy-preserving technologies for machinde learning. He joined Googlew 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.

Leave a Comment or Question

Help us make this conference the best it can be for you. Have questions you'd like this speaker to address? Suggestions for issues that deserve extra attention? Feedback that you'd like to share with the speaker and other attendees?

Join the conversation here (requires login)