Features

Features#

Running a Federation#

OpenFL has multiple options for setting up a federation and running experiments, depending on the users needs.

Task Runner

Define an experiment and distribute it manually. All participants can verify model code and FL plan prior to execution. The federation is terminated when the experiment is finished. Formerly known as the aggregator-based workflow. For more info see TaskRunner API

Workflow Interface (Experimental)

Formulate the experiment as a series of tasks, or a flow. Every flow begins with the start task and concludes with end. Heavily influenced by the interface and design of Netflix’s Metaflow, the popular framework for data scientists. For more info see Workflow API

Aggregation Algorithms#

FedAvg

Paper: McMahan et al., 2017 Default aggregation algorithm in OpenFL. Multiplies local model weights with relative data size and averages this multiplication result.

FedProx

Paper: Li et al., 2020

FedProx in OpenFL is implemented as a custom optimizer for PyTorch/TensorFlow. In order to use FedProx, do the following:

  1. PyTorch:

  • replace your optimizer with SGD-based openfl.utilities.optimizers.torch.FedProxOptimizer

    or Adam-based openfl.utilities.optimizers.torch.FedProxAdam. Also, you should save model weights for the next round via calling .set_old_weights() method of the optimizer before the training epoch.

  1. TensorFlow:

  • replace your optimizer with SGD-based openfl.utilities.optimizers.keras.FedProxOptimizer.

For more details, see ../openfl-tutorials/Federated_FedProx_*_MNIST_Tutorial.ipynb where * is the framework name.

FedOpt

Paper: Reddi et al., 2020

FedOpt in OpenFL: adaptive_aggregation_functions

FedCurv

Paper: Shoham et al., 2019

Requires PyTorch >= 1.9.0. Other frameworks are not supported yet.

Use openfl.utilities.fedcurv.torch.FedCurv to override train function using .get_penalty(), .on_train_begin(), and .on_train_end() methods. In addition, you should override default AggregationFunction of the train task with openfl.interface.aggregation_functions.FedCurvWeightedAverage.

Federated Evaluation#

Evaluate the accuracy and performance of your model on data distributed across decentralized nodes without comprimising data privacy and security. For more info see Federated Evaluation

Privacy Meter#

Quantitatively audit data privacy in statistical and machine learning algorithms. For more info see Privacy Meter

Secure Aggregation#

In Federated Learning (FL), Secure Aggregation (SecAgg) is a technique that allows the participants to collaborate on the central model without revealing their individual contributions (local model updates). For more info see Secure Aggregation

Federated Analytics#

Federated Analytics enables the collection and analysis of data insights across decentralized nodes without compromising data privacy. This feature allows organizations to perform analytics on distributed data while ensuring compliance with privacy regulations. For more info see Federated Analytics

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