# DFL: High-Performance Blockchain-Based Federated Learning

arXiv: https://arxiv.org/abs/2110.15457 · HTML: https://mlsanity.com/paper/2110.15457/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2021.

## Built on by (most cited first, 4 of 4)

- [Vanishing Variance Problem in Fully Decentralized Neural-Network Systems](https://mlsanity.com/paper/2404.04616/) (2024)
- [A Differentially Private Blockchain-Based Approach for Vertical Federated Learning](https://mlsanity.com/paper/2407.07054/) (2024)
- [Collaborative Distributed Machine Learning](https://mlsanity.com/paper/2309.16584/) (2023)
- [A Blockchain-based Platform for Reliable Inference and Training of Large-Scale Models](https://mlsanity.com/paper/2305.04062/) (2023)

## Abstract (arXiv)

Many researchers have proposed replacing the aggregation server in federated learning with a blockchain system to improve privacy, robustness, and scalability. In this approach, clients would upload their updated models to the blockchain ledger and use a smart contract to perform model averaging. However, the significant delay and limited computational capabilities of blockchain systems make it inefficient to support machine learning applications on the blockchain. In this paper, we propose a new public blockchain architecture called DFL, which is specially optimized for distributed federated machine learning. Our architecture inherits the merits of traditional blockchain systems while achieving low latency and low resource consumption by waiving global consensus. To evaluate the performance and robustness of our architecture, we implemented a prototype and tested it on a physical four-node network, and also developed a simulator to simulate larger networks and more complex situations. Our experiments show that the DFL architecture can reach over 90% accuracy for non-I.I.D. datasets, even in the presence of model poisoning attacks, while ensuring that the blockchain part consumes less than 5% of hardware resources.