Privacy-Preserving Asynchronous Vertical Federated Learning Algorithms for Multiparty Collaborative Learning

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 11; pp. 6103 - 6115
• Main Authors: Gu, Bin, Xu, An, Huo, Zhouyuan, Deng, Cheng, Huang, Heng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Asynchronous distributed computation; Collaboration; Collaborative work; Computer applications; Convergence; Convexity; Corporate learning; Data models; Data privacy; Distributed databases; Electronic commerce; Federated learning; Government industry relations; Learning algorithms; Machine learning; Partitioning algorithms; Privacy; privacy-preserving; stochastic gradient descent (SGD); Stochastic processes; Stochasticity; Trusted third parties; vertical federated learning

The privacy-preserving federated learning for vertically partitioned (VP) data has shown promising results as the solution of the emerging multiparty joint modeling application, in which the data holders (such as government branches, private finance, and e-business companies) collaborate throughout...