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Federated learning is a distributed, on-device computation framework that enables training global models without exporting sensitive user data to servers.
- In this work, we describe methods to extend the federation framework to evaluate strategies for personalization of global models.
- We present tools to analyze the effects of personalization and evaluate conditions under which personalization yields desirable models.
- We report on our experiments personalizing a language model for a virtual keyboard for smartphones with a population of tens of millions of users.
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A method for solving the convex programming problem with convergence rate o ( 1 / k 2 ) o(1/{k}^{2})
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