Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications

Understand

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.