For sample participants with the same characteristics, comprehensive operation is realized, and machine learning modeling is carried out by using isomorphism distributed among all parties, which is suitable for scenarios with similar businesses. The effect of training model can be improved by increasing the number of samples
For the data with high sample user overlap and complementary features, the machine learning model is optimized by using gradient descent and other technologies to enrich the sample feature dimensions, which is suitable for cross-industry and cross-institution data union modeling
For the data that cannot meet the horizontal and vertical conditions, there is less user and feature overlap. Therefore, transfer learning technology can be adopted to establish a more widely applicable shared machine learning model
With automatic modeling function, federated learning training and model deployment supporting various machine learning and deep learning, users can easily get started
Comprehensive monitoring of training status and training effect, users can see the training process at a glance
It supports access to multiple databases, coordination parties and decentralized versions, and flexible private deployment
Through federated learning unique algorithm to ensure that data does not go out of the local, shared data minimization, encryption protocol to ensure the security of data interaction
+86 400-993-1619
info@points.org
Zhangjiang Science City, Pudong, Shanghai
Beijing • Shenzhen • Hangzhou
