Federated Learning with Differential Privacy Explained for Advanced Users

Federated Learning with Differential Privacy combines the distributed training of models on multiple local devices with mathematical methods for privacy protection.

In Federated Learning, local models are trained on end devices and only aggregated updates are sent to a central server.

Differential Privacy is used to add noise to these updates so that individual data points cannot be reconstructed.

Important parameters include the privacy budget (ε, delta), which defines how strongly privacy is protected, and the degree of added noise.

This combination enables the development of robust AI models with a high level of data protection, which is especially in demand in regulated areas such as medicine or finance.