System and method for secure knowledge distillation using homomorphic encryption in machine learning environments

Abstract

A method for secure knowledge transfer between machine learning models is provided. The method includes receiving a query within a secure environment, determining whether a local language model within the secure environment is capable of processing the query based on one or more criteria, and generating a response using the local language model when the local language model is determined to be capable of processing the query. An external processing request is generated when the local language model is determined to be not capable of processing the query and the external processing request is encrypted using homomorphic encryption. The encrypted external processing request is transmitted to an external environment and an encrypted response from the external environment is received. The encrypted response is decrypted and knowledge distillation is performed on the decrypted response to extract knowledge patterns; and the local language model is updated using the extracted knowledge patterns.