  Federated Learning enables a population of clients, working with a trusted
server, to collaboratively learn a shared machine learning model while keeping
each client's data within its own local systems. This reduces the risk of
exposing sensitive data, but it is still possible to reverse engineer
information about a client's private data set from communicated model
parameters. Most federated learning systems therefore use differential privacy
to introduce noise to the parameters. This adds uncertainty to any attempt to
reveal private client data, but also reduces the accuracy of the shared model,
limiting the useful scale of privacy-preserving noise. A system can further
reduce the coordinating server's ability to recover private client information,
without additional accuracy loss, by also including secure multiparty
computation. An approach combining both techniques is especially relevant to
financial firms as it allows new possibilities for collaborative learning
without exposing sensitive client data. This could produce more accurate models
for important tasks like optimal trade execution, credit origination, or fraud
detection. The key contributions of this paper are: We present a
privacy-preserving federated learning protocol to a non-specialist audience,
demonstrate it using logistic regression on a real-world credit card fraud data
set, and evaluate it using an open-source simulation platform which we have
adapted for the development of federated learning systems.
