An efficient hierarchical secret sharing for privacy-preserving distributed gradient descent algorithm

This research paper describes a method for protecting privacy in distributed gradient descent (a technique where multiple computers work together to train AI models by each processing part of the data). The authors propose using hierarchical secret sharing (a cryptographic approach where information is split into pieces distributed across multiple parties, so no single party can see the complete data) to keep individual data private while still allowing the AI training process to work efficiently.