Proof of Quality Inference (PoQI): An AI Consensus Protocol for Decentralized DNN Inference Frameworks

In the realm of machine learning systems, achieving consensus among networking nodes is a fundamental yet challenging task. This paper presents Proof of Quality Inference (PoQI), a novel consensus protocol designed to integrate deep learning inference under the basic format of the Practical Byzantine Fault Tolerant (P-BFT) algorithm. PoQI is applied to Deep Neural Networks (DNNs) to infer the quality and authenticity of produced estimations by evaluating the trustworthiness of the DNN node’s decisions. In this manner, PoQI enables DNN inference nodes to reach a consensus on a common DNN inference history in a fully decentralized fashion, rather than relying on a centralized inference decision-making process. Through PBFT adoption, our method ensures byzantine fault tolerance, permitting DNN nodes to reach an agreement on inference validity swiftly and efficiently. We demonstrate the efficacy of PoQI through theoretical analysis and empirical evaluations, highlighting its potential to forge trust among unreliable DNN nodes.