Anonymizing Data for Privacy-Preserving Federated Learning

Understand

Federated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data.

• This is particularly relevant in healthcare applications, where data is rife with personal, highly-sensitive information, and data analysis methods must provably comply with regulatory guidelines.
• Although federated learning prevents sharing raw data, it is still possible to launch privacy attacks on the model parameters that are exposed during the training process, or on the generated machine learning model.
• In this paper, we propose the first syntactic approach for offering privacy in the context of federated learning.