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

By Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, Qiang Yang. Journal of machine learning research, 2021

312 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3d73e21af71bde8dc7984bd72f7077fb691b2523

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yanhong Pu
2. Qian Yang
3. Yaqin Zhang
4. Xiaozhou Ye
5. Ye Ouyang
6. Tianyuan Zou
7. Yuanqin He

Citations in the corpus

• Vertical Federated Learning: Concepts, Advances, and Challenges (2022, background): “Accordingly, many privacy-preserving projects and platforms supporting VFL have been developed in the past three years [9], [10], [11], [12], [13].”
• FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features (2022, unknown): “WeintegratedtheFedBCD-palgorithm intothecurrentFTLimplementationonFATE[41]andsimulate two-partylearningontwomachineswithIntelXeonGoldmodel with20cores,80Gmemoryand1Tharddisk.”