# Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2310.14848 · HTML: https://mlsanity.com/paper/2310.14848/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 54 of its 204 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 30 of 54)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks](https://mlsanity.com/paper/1805.12185/) (2018)
- [Measuring the Intrinsic Dimension of Objective Landscapes](https://mlsanity.com/paper/1804.08838/) (2018)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [Amnesiac Machine Learning](https://mlsanity.com/paper/2010.10981/) (2020)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Certified Defenses for Data Poisoning Attacks](https://mlsanity.com/paper/1706.03691/) (2017)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Can You Really Backdoor Federated Learning?](https://mlsanity.com/paper/1911.07963/) (2019)
- [Machine Unlearning of Features and Labels](https://mlsanity.com/paper/2108.11577/) (2021)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016)
- [Fast Algorithms for Convolutional Neural Networks](https://mlsanity.com/paper/1509.09308/) (2015)
- [Byzantine-Resilient Secure Federated Learning](https://mlsanity.com/paper/2007.11115/) (2020)
- [Data Poisoning Attacks on Federated Machine Learning](https://mlsanity.com/paper/2004.10020/) (2020)
- [Robust Semantic Communications with Masked VQ-VAE Enabled Codebook](https://mlsanity.com/paper/2206.04011/) (2022)
- [Zeno++: Robust Fully Asynchronous SGD](https://mlsanity.com/paper/1903.07020/) (2019)
- [SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud](https://mlsanity.com/paper/1706.10268/) (2017)
- [Compression of Neural Machine Translation Models via Pruning](https://mlsanity.com/paper/1606.09274/) (2016)
- [Prio: Private, Robust, and Scalable Computation of Aggregate Statistics](https://mlsanity.com/paper/1703.06255/) (2017)

## Built on by (most cited first, 18 of 18)

- [A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning](https://mlsanity.com/paper/2502.18535/) (2025)
- [Game of Coding: Sybil Resistant Decentralized Machine Learning with Minimal Trust Assumption](https://mlsanity.com/paper/2410.05540/) (2024)
- [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)
- [FTSmartAudit: A Knowledge Distillation-Enhanced Framework for Automated Smart Contract Auditing Using Fine-Tuned LLMs](https://mlsanity.com/paper/2410.13918/) (2024)
- [DSperse: A Framework for Targeted Verification in Zero-Knowledge Machine Learning](https://mlsanity.com/paper/2508.06972/) (2025)
- [ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation](https://mlsanity.com/paper/2508.18318/) (2025)
- [Combating Knowledge Corruption in Agent Systems: A Byzantine-Tolerant Secure Collaborative RAG Framework](https://mlsanity.com/paper/2608.04366/) (2026)
- [Decentralized Energy Marketplace via NFTs and AI-based Agents](https://mlsanity.com/paper/2311.10406/) (2023)
- [Privacy-Preserving UCB Decision Process Verification via zk-SNARKs](https://mlsanity.com/paper/2404.12186/) (2024)
- [Privacy-Preserving and Trustworthy Deep Learning for Medical Imaging](https://mlsanity.com/paper/2407.00538/) (2024)
- [SoK: Verifiable Cross-Silo FL](https://mlsanity.com/paper/2410.09124/) (2024)
- [Towards Verifiable Federated Unlearning: Framework, Challenges, and The Road Ahead](https://mlsanity.com/paper/2510.00833/) (2025)
- [On the Detectability of Active Gradient Inversion Attacks in Federated Learning](https://mlsanity.com/paper/2511.10502/) (2025)
- [VeriX-Anon: A Multi-Layered Framework for Mathematically Verifiable Outsourced Target-Driven Data Anonymization](https://mlsanity.com/paper/2604.12431/) (2026)
- [AME: A Multi-Type Contributor Attribution Framework in Generative AI Markets](https://mlsanity.com/paper/2606.16075/) (2026)
- [Certification of Machine Learning Models via Directional Sharpness](https://mlsanity.com/paper/2606.25004/) (2026)
- [Privacy-Preserving and Verifiable Approximate Distributed Coded Computing](https://mlsanity.com/paper/2607.02187/) (2026)
- [Physics-Attested Federated Learning: Securing Collaborative Anomaly Detection in Critical Water Infrastructure](https://mlsanity.com/paper/2609.34804/) (2026)

## Abstract (arXiv)

Over recent decades, machine learning has significantly advanced network communication, enabling improved decision-making, user behavior analysis, and fault detection. Decentralized approaches, where participants exchange computation results instead of raw private data, mitigate these risks but introduce challenges related to trust and verifiability. A critical issue arises: How can one ensure the integrity and validity of computation results shared by other participants? Existing survey articles predominantly address security and privacy concerns in decentralized machine learning, whereas this survey uniquely highlights the emerging issue of verifiability. Recognizing the critical role of zero-knowledge proofs in ensuring verifiability, we present a comprehensive review of Zero-Knowledge Proof-based Verifiable Machine Learning (ZKP-VML). To clarify the research problem, we present a definition of ZKP-VML consisting of four algorithms, along with several corresponding key security properties. Besides, we provide an overview of the current research landscape by systematically organizing the research timeline and categorizing existing schemes based on their security properties. Furthermore, through an in-depth analysis of each existing scheme, we summarize their technical contributions and optimization strategies, aiming to uncover common design principles underlying ZKP-VML schemes. Building on the reviews and analysis presented, we identify current research challenges and suggest future research directions. To the best of our knowledge, this is the most comprehensive survey to date on verifiable decentralized machine learning and ZKP-VML.