A journey into the world of federated learning with TensorFlow Federated
Who is this presentation for?
- Machine learning researchers, developers, and practitioners
Krzysztof Ostrowski dives into the key concepts behind FL, an approach to machine learning that allows a shared global model to be trained across many participating clients that keep their training data local. By eliminating the need to collect data at a central location, yet still enabling each participant to benefit from the collective knowledge of everything in the network, federated learning lets you build intelligent applications that leverage insights from data that might be too costly, sensitive, or impractical to collect.
Krzysztof explores how you can develop hands-on familiarity with federated learning using TensorFlow Federated (TFF), a new open source framework in the TensorFlow ecosystem. He outlines the key concepts behind TFF and demonstrates setting up a federated learning experiment and running it in a simulator, what the code looks like and how to extend it, and options for future deployment to real devices.
No matter your background, you’ll find something here for you. If you’re a machine learning developer or practitioner, you can learn how to experiment with your existing machine learning models and data in a federated setting with the Federated Learning API, the included simulation runtime, and sample federated datasets. Researchers who would like to experiment with new types of federated learning algorithms beyond those that come included with the framework or who might wish to develop custom types of federated computations such as statistical analysis of sensitive data will learn how to do so using Federated Core API, a strongly typed functional programming environment that allows for easy mixing of TensorFlow code with federated communication abstractions. And systems engineers and researchers who would like to adapt TensorFlow Federated to target new types of environments will learn the benefits of the abstract platform-independent representation used to represent all computations expressed in TFF. At its core, TFF is designed to facilitate a smooth migration path for all TFF code from a simulation environment to a possible future deployment on real devices in production.
- Familiarity with machine learning concepts and development workflow
- Experience with TensorFlow
What you'll learn
- Understand federated learning: its benefits, how it can be used, how it works, and how to use TFF to gain hands-on experience with it
Krzys Ostrowski is a research scientist at Google AI, focusing on developing programming abstractions for machine learning in large-scale distributed environments. He holds a PhD in computer science from Cornell University, where he focused on distributed systems and programming languages.
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