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

Authors :
Liu, Bo
Tan, Chaowei
Wang, Jiazhou
Zeng, Tao
Shan, Huasong
Yao, Houpu
Huang, Heng
Dai, Peng
Bo, Liefeng
Chen, Yanqing
Publication Year :
2021

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 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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2107.04129
Document Type :
Working Paper