FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection

Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, Qiang Yang.

Year: 2021, Volume: 22, Issue: 226, Pages: 1−6


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.

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