OpenFL: the open federated learning library

Abstract

Objective: Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) and deep learning (DL) projects without sharing sensitive data, such as patient records, financial data, or classified secrets. Approach: Open Federated Learning (OpenFL) framework is an open-source python-based tool for training ML/DL algorithms using the data-private collaborative learning paradigm of FL, irrespective to the use case. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and DL frameworks. Main Results: In this manuscript, we present OpenFL and summarize its motivation and development characteristics, with the intention of facilitating its application to existing ML/DL model training in a production environment. We further provide recommendations to secure a federation using trusted execution environments to ensure explicit model security and integrity, as well as maintain data confidentiality. Finally, we describe the first real-world healthcare federations that use the OpenFL library, and highlight how it can be applied to other non-healthcare use cases. Significance: The OpenFL library is designed for real world scalability, trusted execution, and also prioritizes easy migration of centralized ML models into a federated training pipeline. Although OpenFL's initial use case was in healthcare, it is applicable beyond this domain and is now reaching wider adoption both in research and production settings. The tool is open sourced at github.com/intel/openfl.

Federated learning enables big data for rare cancer boundary detection

“…The data augmentation was done via GaNDLF by leveraging TorchIO 122 . The FL backend developed for this project has been open-sourced as a separate software library, to encourage further research on FL 123 and is available at github.com/ intel/openfl. The optimization of the consensus model inference workload was performed via OpenVINO 124 (github.com/ openvinotoolkit/openvino/tree/2021.4.1), which is an open-source toolkit enabling acceleration of neural network models through various optimization techniques.…”

Transfer learning on structural brain age models to decode cognition in MS: a federated learning approach

“…Currently, open source federated learning frameworks are in full development, such as Flower (23), OpenFL (21) and PySyft (24). Ultimately, technical developments will increase the number of clients that can be present in a federated learning network.…”

Model-Agnostic Federated Learning

“…All three algorithms produce the same strong hypothesis and AdaBoost model, but they differ in the selection of the best weak hypothesis at each round: Fig. 2: OpenFL architecture from [6]. The proposed extension targets only the inner components (coloured in blue).…”

“…The popularity of FL caused the development of a plethora of FL frameworks, e.g., Intel ® OpenFL [6], Flower [5], TensorFlow Federated [1], and HPE Swarm Learning [23] to cite a few. This software only supports one ML model type: Deep Neural Networks (DNNs).…”