Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
This platform addresses privacy concerns in machine learning for developers and researchers, though it is incremental as it builds on existing federated learning concepts.
The authors introduced Fedlearn-Algo, an open-source platform for privacy-preserving machine learning, and demonstrated its efficiency with novel vertical federated learning algorithms like kernel binary classification and random forest models, which outperformed existing models in practice.
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.