The pace of research innovation in federated learning is currently hampered by the relative complexity of properly setting up even simple experiments that reflect practical settings. This issue is exacerbated in academic settings which typically lack access to actual user data.

Recently, multiple open-source projects were created to address this high-barrier to entry. For example, LeaF is a benchmarking framework that contains preprocessed datasets, each with a partitioning that aims to reflect the type of non-identically distributed data partitions encountered in practical federated environments. Federated AI Technology Enabler (FATE) led by WeBank is an open-source technical framework that enables distributed and scalable secure computation protocols based on homomorphic encryption and multi-party computation, supporting federated learning architectures with various machine learning algorithms. Webank is also leading a related IEEE standard proposal. TensorFlow Federated (TFF) led by Google is an open-source framework on top of TensorFlow for flexibly expressing arbitrary computation on decentralized data. TFF enables researchers to experiment with federated learning on their own datasets, or those provided by LeaF. Google has also published a systems paper describing the design of their production system, which supports tens of millions of mobile phones.

We expect these projects will encourage academic researchers and industry engineers to work more closely in addressing the challenges and eventually make significant positive impact. We also hope that this workshop will encourage new benchmarks and open-source projects to enable reproducible federated learning.