Detecting and defending against adversarial attacks in decentralized machine learning systems

Abstract

A system and a method for detecting and defending against adversarial attacks in decentralized learning models are described. The method comprises obtaining a learning parameter for determining a reference cryptographic hash value and a similarity between the data processing nodes (). A cryptographic hash value is determined for each data processing node () based on the learning parameter. The trust score of each data processing node () is updated based on matching of the cryptographic hash value with the reference cryptographic hash value. The learning parameter of each data processing node () is merged to obtain a merged learning parameter based on the trust score. The merged learning parameter is provided to the data processing nodes () to be used for training the machine learning models.