  Federated learning (FL) is a machine learning setting where many clients
(e.g. mobile devices or whole organizations) collaboratively train a model
under the orchestration of a central server (e.g. service provider), while
keeping the training data decentralized. FL embodies the principles of focused
data collection and minimization, and can mitigate many of the systemic privacy
risks and costs resulting from traditional, centralized machine learning and
data science approaches. Motivated by the explosive growth in FL research, this
paper discusses recent advances and presents an extensive collection of open
problems and challenges.
