Ensuring Trustworthy Neural Network Training via Blockchain

Document Type

Conference Proceeding

Publication Date



As Artificial Intelligence prevalence grows, it highlights the risk in relying on compromised models, thereby fueling a growing need to ensure the integrity of trained AI models. In this paper, we present a novel blockchain-based system, designed to authenticate the integrity of trained neural network models. The system addresses the risk of manipulation of a model by strategically re-computing intervals of the training process. Further, the blockchain network provides a traceable, immutable, trusted ledger for cataloging the intricate processes of training and validation. We consider two primary entities involved: ‘submitters’, who submit trained models, and ‘verifiers’, who re-train distinct sections of the submitted models to validate their integrity. The design of the blockchain system emphasizes efficiency by selectively targeting a portion of all training intervals. This is made possible through the use of an innovative weight-analysis algorithm, which applies an Absolute Change approach to identify outliers. We implement our solution to demonstrate that the proposed blockchain system is robust, and the weight-analysis algorithm is accurate and scalable.