9:00 - 9:20 AM | Welcome and Introduction to Federated Learning

_{Peter Kairouz & Krzysztof Ostrowski}, Google

• _Welcome
• _{Introduction to Federated Learning}
• _{Overview of tutorials & speakers}

9:20 - 10:50 AM | Introduction to Federated Learning and Analytics with TFF + Q&A

_{Hosted by: Nova Fallen & Emily Glanz, Google}

_{Sometimes centrally collecting data produced by edge devices, such as mobile phones or cars, is infeasible or undesirable. With federated learning, clients collaboratively train a model under the direction of a server, while keeping the training data decentralized and mitigating privacy risks. Learn how you can use TensorFlow Federated, an open-source framework for machine learning and other computations on decentralized data, to explore federated learning. In this workshop, you will train an image classification model and a text generation model while learning about the unique advantages and challenges of the federated setting. You will also see examples of how TFF can be used to enable new research. After this tutorial, you will be equipped to further experiment with federated learning on your own.} 

• Image Classification in TFF
• _{Text Generation in TFF}

10:50 - 11:00 AM | Break

 

11:00 AM - 12:30 PM | TFF for Federated Learning Research + Q&A

_{Hosted by: Zachary Charles and Weikang Song, Google}

_{TFF is an extensible, powerful framework for conducting federated learning (FL) research by simulating federated computations on realistic proxy datasets. In this tutorial session, we'll describe the main concepts, components and detailed guidance for conducting different kinds of research in TFF. We'll demonstrate optimization and compression algorithms as examples but also introduce advanced techniques for customizing TFF for various research needs. This tutorial also served as a great starting point for people who are interested to explore the TFF research directory. Note that this tutorial session assumes the audience is familiar with FL layers of TensorFlow Federated.}

• _{Optimization methods for Federated Learning}
• _{Model and update compression}
• _{Building your own Federated Learning algorithm}