# zkDFL: An efficient and privacy-preserving decentralized federated learning with zero-knowledge proof

arXiv: https://arxiv.org/abs/2312.04579 · HTML: https://mlsanity.com/paper/2312.04579/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 59 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus](https://mlsanity.com/paper/2004.00773/) (2020)
- [When Machine Learning Meets Privacy: A Survey and Outlook](https://mlsanity.com/paper/2011.11819/) (2020)
- [Federated Model Distillation with Noise-Free Differential Privacy](https://mlsanity.com/paper/2009.05537/) (2020)
- [BAFFLE : Blockchain Based Aggregator Free Federated Learning](https://mlsanity.com/paper/1909.07452/) (2019)
- [Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks](https://mlsanity.com/paper/2011.09902/) (2020)
- [VeriML: Enabling Integrity Assurances and Fair Payments for Machine Learning as a Service](https://mlsanity.com/paper/1909.06961/) (2019)
- [An Overview on Smart Contracts: Challenges, Advances and Platforms](https://mlsanity.com/paper/1912.10370/) (2019)
- [Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System](https://mlsanity.com/paper/2111.06290/) (2021)
- [Preserving Privacy and Security in Federated Learning](https://mlsanity.com/paper/2202.03402/) (2022)
- [FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof](https://mlsanity.com/paper/2305.04507/) (2023)
- [Advancing Blockchain-based Federated Learning through Verifiable Off-chain Computations](https://mlsanity.com/paper/2206.11641/) (2022)

## Built on by (most cited first, 1 of 1)

- [Verifiable Split Learning via zk-SNARKs](https://mlsanity.com/paper/2511.01356/) (2025)

## Abstract (arXiv)

Federated learning (FL) has been widely adopted in various fields of study and business. Traditional centralized FL systems suffer from serious issues. To address these concerns, decentralized federated learning (DFL) systems have been introduced in recent years. With the help of blockchains, they attempt to achieve more integrity and efficiency. However, privacy preservation remains an uncovered aspect of these systems. To tackle this, as well as to scale the blockchain-based computations, we propose a zero-knowledge proof (ZKP)-based aggregator (zkDFL). This allows clients to share their large-scale model parameters with a trusted centralized server without revealing their individual data to other clients. We utilize blockchain technology to manage the aggregation algorithm via smart contracts. The server performs a ZKP algorithm to prove to the clients that the aggregation is done according to the accepted algorithm. Additionally, the server can prove that all inputs from clients have been used. We evaluate our approach using a public dataset related to the wearable Internet of Things. As demonstrated by numerical evaluations, zkDFL introduces verifiability of the correctness of the aggregation process and enhances the privacy protection and scalability of DFL systems, while the gas cost has significantly declined.