Abstract
PySyft is an open-source multi-language library enabling secure and private machine learning by wrapping and extending popular deep learning frameworks such as PyTorch in a transparent, lightweight, and user-friendly manner. Its aim is to both help popularize privacy-preserving techniques in machine learning by making them as accessible as possible via Python bindings and common tools familiar to researchers and data scientists, as well as to be extensible such that new Federated Learning (FL), Multi-Party Computation, or Differential Privacy methods can be flexibly and simply implemented and integrated. This chapter will introduce the methods available within the PySyft library and describe their implementations. We will then provide a proof-of-concept demonstration of a FL workflow using an example of how to train a convolutional neural network. Next, we review the use of PySyft in academic literature to date and discuss future use-cases and development plans. Most importantly, we introduce Duet: our tool for easier FL for scientists and data owners.
We thank the OpenMined community and contributors for their work making PySyft possible. For more information about OpenMined, find us on GitHub or slack. https://www.openmined.org/.
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Ziller, A. et al. (2021). PySyft: A Library for Easy Federated Learning. In: Rehman, M.H.u., Gaber, M.M. (eds) Federated Learning Systems. Studies in Computational Intelligence, vol 965. Springer, Cham. https://doi.org/10.1007/978-3-030-70604-3_5
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