Differentially Private Federated Learning using Opacus, PyTorch and Flower#

This example contains code demonstrating how to include the Opacus library for training a model using DP-SGD. The code is adapted from multiple other examples:

  • PyTorch Quickstart

  • Simulation Quickstart

  • Simulation Extended Example


  • Flower nightly release (or development version from main branch) for the simulation, otherwise normal Flower for the client

  • PyTorch 1.7.1 (but most likely will work with older versions)

  • Ray 1.4.1 (just for the simulation)

  • Opacus 0.14.0

Privacy Parameters#

The parameters can be set in dp_cifar_main.py.

Running the client#

Run the server with python server.py. Then open two (or more) new terminals to start two (or more) clients with python dp_cifar_client.py.

Running the simulation#

Note: It is not possible to see the total privacy budget used with this example since the simulation creates clients from scratch every round.

Run the simulation with python dp_cifar_simulation.py.