# A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning

arXiv: https://arxiv.org/abs/2502.18535 · HTML: https://mlsanity.com/paper/2502.18535/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 88 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 22 of 22)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [Privacy Preserving Machine Learning: Threats and Solutions](https://mlsanity.com/paper/1804.11238/) (2018)
- [Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2](https://mlsanity.com/paper/2210.00586/) (2022)
- [zkLLM: Zero Knowledge Proofs for Large Language Models](https://mlsanity.com/paper/2404.16109/) (2024)
- [SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud](https://mlsanity.com/paper/1706.10268/) (2017)
- [Scaling up Trustless DNN Inference with Zero-Knowledge Proofs](https://mlsanity.com/paper/2210.08674/) (2022)
- [VeriML: Enabling Integrity Assurances and Fair Payments for Machine Learning as a Service](https://mlsanity.com/paper/1909.06961/) (2019)
- [Machine learning and deep learning](https://mlsanity.com/paper/2104.05314/) (2021)
- [pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing](https://mlsanity.com/paper/2201.09186/) (2022)
- [zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training](https://mlsanity.com/paper/2307.16273/) (2023)
- [Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey](https://mlsanity.com/paper/2310.14848/) (2023)
- [ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline](https://mlsanity.com/paper/2212.05428/) (2022)
- [Zero-Knowledge Proof-based Practical Federated Learning on Blockchain](https://mlsanity.com/paper/2304.05590/) (2023)
- [Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets](https://mlsanity.com/paper/1902.03785/) (2019)
- [Artemis: Efficient Commit-and-Prove SNARKs for zkML](https://mlsanity.com/paper/2409.12055/) (2024)
- [Trust the Process: Zero-Knowledge Machine Learning to Enhance Trust in Generative AI Interactions](https://mlsanity.com/paper/2402.06414/) (2024)
- [Zero-Knowledge Proof Frameworks: A Systematic Survey](https://mlsanity.com/paper/2502.07063/) (2025)

## Built on by (most cited first, 22 of 22)

- [Embodied AI: Emerging Risks and Opportunities for Policy Action](https://mlsanity.com/paper/2509.00117/) (2025)
- [VET Your Agent: Towards Host-Independent Autonomy via Verifiable Execution Traces](https://mlsanity.com/paper/2512.15892/) (2025)
- [A Taxonomy of Attacks and Defenses in Split Learning](https://mlsanity.com/paper/2505.05872/) (2025)
- [textttRange-Arithmetic: Verifiable Deep Learning Inference on an Untrusted Party](https://mlsanity.com/paper/2505.17623/) (2025)
- [Verifiable Fine-Tuning for LLMs: Zero-Knowledge Training Proofs Bound to Data Provenance and Policy](https://mlsanity.com/paper/2510.16830/) (2025)
- [The Rise of Sparse Mixture-of-Experts: A Survey from Algorithmic Foundations to Decentralized Architectures and Vertical Domain Applications](https://mlsanity.com/paper/2602.08019/) (2026)
- [Hawkeye: Reproducing GPU-Level Non-Determinism](https://mlsanity.com/paper/2603.20421/) (2026)
- [DSperse: A Framework for Targeted Verification in Zero-Knowledge Machine Learning](https://mlsanity.com/paper/2508.06972/) (2025)
- [VeriLoRA: Fine-Tuning Large Language Models with Verifiable Security via Zero-Knowledge Proofs](https://mlsanity.com/paper/2508.21393/) (2025)
- [AVEC: Bootstrapping Privacy for Local LLMs](https://mlsanity.com/paper/2509.10561/) (2025)
- [Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications](https://mlsanity.com/paper/2509.25072/) (2025)
- [TRUST: A Decentralized Framework for Auditing Large Language Model Reasoning](https://mlsanity.com/paper/2510.20188/) (2025)
- [Counterweights and Complementarities: The Convergence of AI and Blockchain Powering a Decentralized Future](https://mlsanity.com/paper/2603.11299/) (2026)
- [Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates](https://mlsanity.com/paper/2604.04738/) (2026)
- [The Open-Weight Paradox: Why Restricting Access to AI Models May Undermine the Safety It Seeks to Protect](https://mlsanity.com/paper/2604.17413/) (2026)
- [Crypto x AI, AI x Crypto: A Survey](https://mlsanity.com/paper/2606.13892/) (2026)
- [On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation](https://mlsanity.com/paper/2510.16024/) (2025)
- [JSTprove: Pioneering Verifiable AI for a Trustless Future](https://mlsanity.com/paper/2510.21024/) (2025)
- [ZK-SenseLM: Verifiable Large-Model Wireless Sensing with Selective Abstention and Zero-Knowledge Attestation](https://mlsanity.com/paper/2510.25677/) (2025)
- [Making AI-Assisted Grant Evaluation Auditable without Exposing the Model](https://mlsanity.com/paper/2604.25200/) (2026)
- [Communication-Efficient Verifiable Attention for LLM Inference](https://mlsanity.com/paper/2606.16352/) (2026)
- [Physics-Attested Federated Learning: Securing Collaborative Anomaly Detection in Critical Water Infrastructure](https://mlsanity.com/paper/2609.34804/) (2026)

## Abstract (arXiv)

Machine learning is increasingly deployed through outsourced and cloud-based pipelines, which improve accessibility but also raise concerns about computational integrity, data privacy, and model confidentiality. Zero-knowledge proofs (ZKPs) provide a compelling foundation for verifiable machine learning because they allow one party to certify that a training, testing, or inference result was produced by the claimed computation without revealing sensitive data or proprietary model parameters. Despite rapid progress in zero-knowledge machine learning (ZKML), the literature remains fragmented across different cryptographic settings, ML tasks, and system objectives. This survey presents a comprehensive review of ZKML research published from June 2017 to August 2025. We first introduce the basic ZKP formulations underlying ZKML and organize existing studies into three core tasks: verifiable training, verifiable testing, and verifiable inference. We then synthesize representative systems, compare their design choices, and analyze the main implementation bottlenecks, including limited circuit expressiveness, high proving cost, and deployment complexity. In addition, we summarize major techniques for improving generality and efficiency, review emerging commercial efforts, and discuss promising future directions. By consolidating the design space of ZKML, this survey aims to provide a structured reference for researchers and practitioners working on trustworthy and privacy-preserving machine learning.