Presented By O’Reilly and Intel AI
Put AI to work
8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

Federated learning

Ryan Micallef (Cloudera Fast Forward Labs)
11:55–12:35 Wednesday, 10 October 2018
Models and Methods
Location: King's Suite - Balmoral
Secondary topics:  Deep Learning models, Ethics, Privacy, and Security
Average rating: ****.
(4.00, 1 rating)

Who is this presentation for?

  • Machine learning engineers, data scientists, and vice presidents of innovation and product

Prerequisite knowledge

  • A basic understanding of machine learning

What you'll learn

  • Understand what makes a problem federated (general principles and specific examples)
  • Explore algorithmic challenges in this setting and their solutions
  • Learn how to build a federated ML product

Description

Federated learning is distributed machine learning across edge devices with a number of twists that make it both challenging and broadly applicable. Training happens on the same devices that generate the data. Those edge users are often concerned about privacy and are thus unwilling to share their training data. And even when they’re willing to share the data, communication is unreliable and slow, so it may not be practical. Examples include predictive text on cell phones, a person’s engagement with their own photos, and machine learning in the browser applied to corporate text archives such as a team Slack or Google Drive, and ML on low-powered field devices in energy, agriculture, and logistics. The principles of data minimization established by the GDPR and the prevalence of smart sensors makes these use cases more common and the advantages of federated learning more compelling.

Ryan Micallef discusses the algorithmic and production techniques of federated learning and the privacy-preserving, fault-tolerant product opportunities they offer.

Photo of Ryan Micallef

Ryan Micallef

Cloudera Fast Forward Labs

Ryan Micallef is a research engineer at Cloudera Fast Forward Labs focused on studying emerging machine learning technologies and helping clients apply them. Ryan is also an attorney barred in New York and spent nearly a decade as an intellectual property litigator focused on technical cases. Ryan holds a bachelor’s degree in computer science from Georgia Tech and a JD from Brooklyn Law School. He spends his free time soldering circuits and wrenching motorcycles. He also teaches microcontroller programming at his local hackerspace, NYC Resistor.

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)