Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform

• URL: http://arxiv.org/abs/2107.04129v1
• Date: Thu, 8 Jul 2021 21:59:56 GMT
• Title: Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
• Authors: Bo Liu, Chaowei Tan, Jiazhou Wang, Tao Zeng, Huasong Shan, Houpu Yao, Huang Heng, Peng Dai, Liefeng Bo, Yanqing Chen
• Abstract summary: 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.
• Score: 15.198116661595487
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 transfering widely used data formats between machines. We will maintain this platform by adding more functional modules and algorithm examples.

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