FATE: An industrial grade platform for collaborative learning with data protection

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作者
Liu, Yang [1 ]
Fan, Tao [2 ]
Chen, Tianjian [3 ]
Xu, Qian [2 ,3 ]
Yang, Qiang [2 ,3 ]
机构
[1] Institute for AI Industry Research, Tsinghua University, Beijing, China
[2] AI Department of Webank, Shenzhen, China
[3] Hong Kong University of Science and Technology, Hong Kong, Hong Kong
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Collaborative and federated learning has become an emerging solution to many industrial applications where data values from different sites are exploit jointly with privacy protection. We introduce FATE, an industrial-grade project that supports enterprises and institutions to build machine learning models collaboratively at large-scale in a distributed manner. FATE supports a variety of secure computation protocols and machine learning algorithms, and features out-of-box usability with end-to-end building modules and visualization tools. Documentations are available at https://github.com/FederatedAI/FATE. Case studies and other information are available at https://www.fedai.org. © 2021 Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, Qiang Yang.
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