Additional Examples for NVIDIA FLARE Job API

you have probably already looked at getting started examples, and hello-world examples. Here are additional examples for advanced algorithms

Basic Concepts

At the heart of NVFlare lies the concept of collaboration through tasks. An FL controller assigns tasks (e.g., training on local data) to one or more FL clients, processes returned results (e.g., model weight updates), and may assign additional tasks based on these results and other factors (e.g., a pre-configured number of training rounds). The clients run executors that can listen for tasks and perform the necessary computations locally, such as model training. This task-based interaction repeats until the experiment’s objectives are met.

We can also add data filters (for example, for homomorphic encryption or differential privacy filters) to the task data or results received or produced by the server or clients.

Examples

We have several examples to illustrate job APIs Each example folder includes basic job configurations for running different FL algorithms, such as FedOpt and SCAFFOLD.

1. PyTorch Examples

2. Tensorflow Examples

3. Scikit-Learn Examples

Note

More examples can be found at https://nvidia.github.io/NVFlare.