# FL Algorithms ## Federated Averaging In NVIDIA FLARE, FedAvg is implemented through the [Scatter and Gather Workflow](../programming_guide/controllers/scatter_and_gather_workflow.md#scatter-and-gather-workflow). In the federated averaging workflow, a set of initial weights is distributed to client workers who perform local training. After local training, clients return their local weights as a Shareables that are aggregated (averaged). This new set of global average weights is redistributed to clients and the process repeats for the specified number of rounds. ## FedProx [FedProx](https://arxiv.org/abs/1812.06127) implements a `Loss function` to penalize a client’s local weights based on deviation from the global model. An example configuration can be found in cifar10_fedprox of the [CIFAR-10 example](https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/cifar10). ## FedOpt [FedOpt](https://arxiv.org/abs/2003.00295) implements a `ShareableGenerator` that can use a specified Optimizer and Learning Rate Scheduler when updating the global model. An example configuration can be found in cifar10_fedopt of [CIFAR-10 example](https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/cifar10). ## SCAFFOLD [SCAFFOLD](https://arxiv.org/abs/1910.06378) uses a slightly modified version of the CIFAR-10 Learner implementation, namely the CIFAR10ScaffoldLearner, which adds a correction term during local training following the [implementation](https://github.com/Xtra-Computing/NIID-Bench) as described in [Li et al.](https://arxiv.org/abs/2102.02079). An example configuration can be found in cifar10_scaffold of [CIFAR-10 example](https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/cifar10). ## Federated XGBoost NVFlare supports federated learning using popular gradient boosting library XGBoost. It uses XGBoost library with federated plugin (xgboost version >= 1.7.0rc1) to perform the learning. Using XGBoost with NVFlare has following benefits compared with running federated XGBoost directly, * XGBoost instance’s life-cycle is managed by NVFlare. Both XGBoost client and server are started/stopped automatically by NVFlare workflow. * For histogram-based XGBoost federated server can be configured automatically with auto-assigned port number. * When mutual TLS is used, the certificates are managed by NVFlare using existing provisioning process. * No need to manually configure each instance. Instance specific parameters like code:rank are assigned automatically by the NVFlare controller. * [Federated Horizontal XGBoost (GitHub)](https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/xgboost) - Includes examples of histogram-based and tree-based algorithms. Tree-based algorithms also includes bagging and cyclic approaches * [Federated Vertical XGBoost (GitHub)](https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/vertical_xgboost) - Example using Private Set Intersection and XGBoost on vertically split HIGGS data. ## Federated Analytics * [Federated Statistics for medical imaging (Github)](https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/federated-statistics/image_stats/README.md) - Example of gathering local image histogram to compute the global dataset histograms. * [Federated Statistics for tabular data with DataFrame (Github)](https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/federated-statistics/df_stats/README.md) - Example of gathering local statistics summary from Pandas DataFrame to compute the global dataset statistics. * [Federated Statistics with Monai Statistics integration for Spleen CT Image (Github)](https://github.com/NVIDIA/NVFlare/tree/main/integration/monai/examples/spleen_ct_segmentation_local/README.md) - Example demonstrated Monai statistics integration and few other features in federated statistics