NeuroChainOps: A Privacy-Preserving, Blockchain-Backed MLOps Framework for Federated Neural Architecture Search

Federated Neural Architecture Search (FNAS) enables collaborative optimization of neural network architectures across distributed nodes without centralizing sensitive data. However, exist- ing FNAS approaches face critical challenges in ensuring model integrity, preserving privacy, and providing verifiable trust in distributed MLOps pipelines. We present NeuroChainOps, a novel framework that integrates blockchain technology with zero-knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs) to create a privacy-preserving, verifiable MLOps infras- tructure for federated neural architecture search. Our approach combines distributed architecture optimization with cryptographic verification mechanisms, ensuring both scalability and security in collaborative machine learning environments. We formalize the theoretical foundations of privacy-preserving FNAS with blockchain verification and provide comprehensive experimental validation across CIFAR-10, CIFAR-100, MNIST, and ImageNet datasets. Results demonstrate that NeuroChainOps achieves comparable accuracy to centralized NAS while reducing communi- cation overhead by 34% and providing cryptographic guarantees of model integrity. This work represents the first integration of FNAS, blockchain, and zk-SNARKs for privacy-preserving MLOps, establishing a new paradigm for trustworthy distributed AI systems.