  Federated learning (FL) enables training models at different sites and
updating the weights from the training instead of transferring data to a
central location and training as in classical machine learning. The FL
capability is especially important to domains such as biomedicine and smart
grid, where data may not be shared freely or stored at a central location
because of policy challenges. Thanks to the capability of learning from
decentralized datasets, FL is now a rapidly growing research field, and
numerous FL frameworks have been developed. In this work, we introduce APPFL,
the Argonne Privacy-Preserving Federated Learning framework. APPFL allows users
to leverage implemented privacy-preserving algorithms, implement new
algorithms, and simulate and deploy various FL algorithms with
privacy-preserving techniques. The modular framework enables users to customize
the components for algorithms, privacy, communication protocols, neural network
models, and user data. We also present a new communication-efficient algorithm
based on an inexact alternating direction method of multipliers. The algorithm
requires significantly less communication between the server and the clients
than does the current state of the art. We demonstrate the computational
capabilities of APPFL, including differentially private FL on various test
datasets and its scalability, by using multiple algorithms and datasets on
different computing environments.
