# Zero-Knowledge Proof-based Practical Federated Learning on Blockchain

arXiv: https://arxiv.org/abs/2304.05590 · HTML: https://mlsanity.com/paper/2304.05590/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2023.

## Built on by (most cited first, 10 of 10)

- [A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning](https://mlsanity.com/paper/2502.18535/) (2025)
- [Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey](https://mlsanity.com/paper/2310.14848/) (2023)
- [On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions](https://mlsanity.com/paper/2508.13730/) (2025)
- [Zero-Knowledge Federated Learning: A New Trustworthy and Privacy-Preserving Distributed Learning Paradigm](https://mlsanity.com/paper/2503.15550/) (2025)
- [Blockchain-Enabled Federated Learning](https://mlsanity.com/paper/2508.06406/) (2025)
- [A Blockchain-based Platform for Reliable Inference and Training of Large-Scale Models](https://mlsanity.com/paper/2305.04062/) (2023)
- [Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies](https://mlsanity.com/paper/2308.02219/) (2023)
- [End-to-End Verifiable Decentralized Federated Learning](https://mlsanity.com/paper/2404.12623/) (2024)
- [A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions](https://mlsanity.com/paper/2605.11010/) (2026)
- [PoCoFL: POlicy-COmpliant Federated Learning](https://mlsanity.com/paper/2610.03650/) (2026)

## Abstract (arXiv)

Since the concern of privacy leakage extremely discourages user participation in sharing data, federated learning has gradually become a promising technique for both academia and industry for achieving collaborative learning without leaking information about the local data. Unfortunately, most federated learning solutions cannot efficiently verify the execution of each participant's local machine learning model and protect the privacy of user data, simultaneously. In this article, we first propose a Zero-Knowledge Proof-based Federated Learning (ZKP-FL) scheme on blockchain. It leverages zero-knowledge proof for both the computation of local data and the aggregation of local model parameters, aiming to verify the computation process without requiring the plaintext of the local data. We further propose a Practical ZKP-FL (PZKP-FL) scheme to support fraction and non-linear operations. Specifically, we explore a Fraction-Integer mapping function, and use Taylor expansion to efficiently handle non-linear operations while maintaining the accuracy of the federated learning model. We also analyze the security of PZKP-FL. Performance analysis demonstrates that the whole running time of the PZKP-FL scheme is approximately less than one minute in parallel execution.