# BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework

arXiv: https://arxiv.org/abs/2205.10568 · HTML: https://mlsanity.com/paper/2205.10568/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2022.

## Built on by (most cited first, 11 of 11)

- [Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes](https://mlsanity.com/paper/2312.06353/) (2023)
- [dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis](https://mlsanity.com/paper/2503.10412/) (2025)
- [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024)
- [Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework](https://mlsanity.com/paper/2511.21686/) (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)
- [Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning](https://mlsanity.com/paper/2501.04453/) (2025)
- [Hubs and Spokes Learning: Efficient and Scalable Collaborative Machine Learning](https://mlsanity.com/paper/2504.20988/) (2025)
- [Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning](https://mlsanity.com/paper/2506.20413/) (2025)
- [DeRelayL: Sustainable Decentralized Relay Learning](https://mlsanity.com/paper/2605.02935/) (2026)
- [Integrity of peer-to-peer distributed LLM inference under malicious nodes](https://mlsanity.com/paper/2607.19490/) (2026)

## Abstract (arXiv)

Federated learning (FL) enables collaborative training of machine learning models without sharing training data. Traditional FL heavily relies on a trusted centralized server. Although decentralized FL eliminates the central dependence, it may worsen the other inherit problems faced by FL such as poisoning attacks and data representation leakage due to insufficient restrictions on the behavior of participants, and heavy communication cost, especially in fully decentralized scenarios, i.e., peer-to-peer (P2P) settings. In this paper, we propose a blockchain-based fully decentralized P2P framework for FL, called BlockDFL. It takes blockchain as the foundation, leveraging the proposed PBFT-based voting mechanism and two-layer scoring mechanism to coordinate FL among peer participants without mutual trust, while effectively defending against poisoning attacks. Gradient compression is introduced to lowering communication cost and prevent data from being reconstructed from transmitted model updates. Extensive experiments conducted on two real-world datasets exhibit that BlockDFL obtains competitive accuracy compared to centralized FL and can defend poisoning attacks while achieving efficiency and scalability. Especially when the proportion of malicious participants is as high as 40%, BlockDFL can still preserve the accuracy of FL, outperforming existing fully decentralized P2P FL frameworks based on blockchain.