An emerging approach to machine learning, called federated learning, enables machine learning on decentralized datasets. This approach can help protect data privacy while also improving local speed and performance. This experiment allows you to speak with Google experts about your work in federated learning, including the use of the open-source TensorFlow Federated library.
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Federated learning is a distributed machine learning approach that trains machine learning models using decentralized examples residing on devices such as smartphones. It is already used to power features in Google’s virtual keyboard for mobile devices (Gboard) including query suggestions, next word prediction, and emoji prediction. In this experiment, we’d like to brainstorm with selected customers about whether and how federated learning would be useful for their use cases, and what infrastructure would be needed to take advantage of this technology.
The format of this experiment is direct conversation with the federated learning team members at Google AI (video or phone conference). This conversation will help customers understand how federated learning and decentralized data approaches can be helpful for their use cases, and also help Google plan for making this technology more easily available to customers.
As part of the application to participate in this experiment, we will ask you about your use case, data types, and/or other relevant questions to ensure that the experiment is a good fit for you.
What skills do I need?
Users of this experiment should be comfortable with using TensorFlow with machine learning, and understand the end-to-end data lifecycle for their applications.