去中心化机器学习中计算模型的验证损失的系统和方法
By merging and validating model parameters in a decentralized environment through distributed machine learning and blockchain technology, the problem of model training and validation in managing complex distributed enterprise infrastructure is solved, achieving data privacy protection and system scalability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HEWLETT PACKARD ENTERPRISE DEV LP
- Filing Date
- 2021-04-22
- Publication Date
- 2026-07-17
AI Technical Summary
In decentralized distributed enterprise infrastructures, the process of managing and validating machine learning models is complex and difficult to implement, especially in the absence of a central server. How can we ensure data privacy protection and decentralized control while training and validating models?
By employing distributed machine learning and blockchain technology, model parameters and loss values are merged and verified among nodes by merging leaders and decryptors. Homomorphic encryption is used to protect privacy, and decentralized management and consensus mechanisms are achieved through blockchain to ensure the system's security and scalability.
It enables efficient model training and validation in a decentralized environment, protects data privacy, provides greater fault tolerance and system scalability, and avoids the risk of single point of failure.
Smart Images
Figure CN113837392B_ABST