Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients collaboratively train a model under the orchestration of a central server, 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.