  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, the scale and heterogeneity of federated
data presents new challenges in research areas such as federated learning,
meta-learning, and multi-task learning. As the machine learning community
begins to tackle these challenges, we are at a critical time to ensure that
developments made in these areas are grounded with realistic benchmarks. 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.
