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.
@article{arxiv.2107.04129,
title = {Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform},
author = {Bo Liu and Chaowei Tan and Jiazhou Wang and Tao Zeng and Huasong Shan and Houpu Yao and Heng Huang and Peng Dai and Liefeng Bo and Yanqing Chen},
journal= {arXiv preprint arXiv:2107.04129},
year = {2021}
}