In this paper, we present Fedlearn-Algo, an open-source privacy preserving machine learning platform. We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms. As the first batch of novel FL algorithm examples, we release vertical federated kernel binary classification model and vertical federated random forest model. They have been tested to be more efficient than existing vertical federated learning models in our practice. Besides the novel FL algorithm examples, we also release a machine communication module. The uniform data transfer interface supports transferring widely used data formats between machines. We will maintain this platform by adding more functional modules and algorithm examples. The code is available at https://github.com/fedlearnAI/fedlearn-algo.
翻译:在本文中,我们介绍Fedlearn-Algo(一个开放源码隐私保护机器学习平台),我们利用这个平台展示我们在隐私保护机器学习算法方面的研究和发展结果。作为第一批新的FL算法实例,我们发布了纵向联合内核二进制分类模型和纵向联合随机森林模型,测试它们比我们实践中现有的纵向联合学习模型更有效。除了新的FL算法示例外,我们还发布了一个机器通信模块。统一的数据传输界面支持在机器之间传输广泛使用的数据格式。我们将通过增加更多功能模块和算法示例来维护这个平台。代码可在https://github.com/fedlearnAI/fedlearn-algo查阅。