OpenFL: 开放源码的联邦学习框架 (OpenFL: An open-source framework for Federated Learning)

G Anthony Reina,Alexey Gruzdev,Patrick Foley,Olga Perepelkina,Mansi Sharma,Igor Davidyuk,Ilya Trushkin,Maksim Radionov,Aleksandr Mokrov,Dmitry Agapov,Jason Martin,Brandon Edwards,Micah J. Sheller,Sarthak Pati,Prakash Narayana Moorthy,Shih-han Wang,Prashant Shah,Spyridon Bakas

Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets. Open Federated Learning (OpenFL https://github.com/intel/openfl) is an open-source framework for training ML algorithms using the data-private collaborative learning paradigm of FL. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and deep learning frameworks. Here, we summarize the motivation and development characteristics of OpenFL, with the intention of facilitating its application to existing ML model training in a production environment. Finally, we describe the first use of the OpenFL framework to train consensus ML models in a consortium of international healthcare organizations, as well as how it facilitates the first computational competition on FL.

翻译：联邦学习(FL)是一种计算模式,使各组织能够在不分享敏感数据(如病人记录、财务数据或机密)的情况下就机器学习(ML)项目进行合作。开放联邦学习(OpenFLL https://github.com/intel/openfl)是利用FL的数据-私营合作学习模式来培训ML算法的开放源框架。OpenFL与TensorFlow和PyTorch共同建立的培训管道合作,可以很容易地扩展到其他ML和深层学习框架。这里,我们总结OpenFL的动机和发展特点,目的是促进将其应用于现有的ML模型培训。最后,我们介绍了开放联邦学习框架首次在国际保健组织联合会中培训协商一致的ML模型,以及它如何促进首次计算FL的竞赛。

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联邦学习（Federated Learning）是一种新兴的人工智能基础技术，在 2016 年由谷歌最先提出，原本用于解决安卓手机终端用户在本地更新模型的问题，其设计目标是在保障大数据交换时的信息安全、保护终端数据和个人数据隐私、保证合法合规的前提下，在多参与方或多计算结点之间开展高效率的机器学习。其中，联邦学习可使用的机器学习算法不局限于神经网络，还包括随机森林等重要算法。联邦学习有望成为下一代人工智能协同算法和协作网络的基础。