Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day. This wealth of data can help to learn models that can improve the user experience on each device. However, learning in federated settings presents new challenges at all stages of the machine learning pipeline. As the machine learning community begins to tackle these challenges, we are at a critical time to ensure that developments made in this area are grounded in real-world assumptions. To this end, we propose LEAF, a modular benchmarking framework for learning in federated settings. LEAF includes a suite of open-source federated datasets, a rigorous evaluation framework, and a set of reference implementations, all geared towards capturing the obstacles and intricacies of practical federated environments.