# A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus

arXiv: https://arxiv.org/abs/2004.00773 · HTML: https://mlsanity.com/paper/2004.00773/

## Key facts

- Cited by 84 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2020.

## Built on by (most cited first, 30 of 84)

- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [FedGL: Federated Graph Learning Framework with Global Self-Supervision](https://mlsanity.com/paper/2105.03170/) (2021)
- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey](https://mlsanity.com/paper/2302.02573/) (2023)
- [FS-Real: Towards Real-World Cross-Device Federated Learning](https://mlsanity.com/paper/2303.13363/) (2023)
- [A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee](https://mlsanity.com/paper/2108.00365/) (2021)
- [Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness](https://mlsanity.com/paper/2108.03980/) (2021)
- [A Systematic Survey of Blockchained Federated Learning](https://mlsanity.com/paper/2110.02182/) (2021)
- [Trustworthy Federated Learning via Blockchain](https://mlsanity.com/paper/2209.04418/) (2022)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus](https://mlsanity.com/paper/2101.03300/) (2021)
- [Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond](https://mlsanity.com/paper/2306.00419/) (2023)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things](https://mlsanity.com/paper/2104.10501/) (2021)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [Defending Against Poisoning Attacks in Federated Learning with Blockchain](https://mlsanity.com/paper/2307.00543/) (2023)
- [Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning](https://mlsanity.com/paper/2201.05286/) (2022)
- [BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning](https://mlsanity.com/paper/2202.02817/) (2022)
- [Privacy-Preserving in Blockchain-based Federated Learning Systems](https://mlsanity.com/paper/2401.03552/) (2024)
- [Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent](https://mlsanity.com/paper/2104.08100/) (2021)
- [FLoBC: A Decentralized Blockchain-Based Federated Learning Framework](https://mlsanity.com/paper/2112.11873/) (2021)
- [Network Gradient Descent Algorithm for Decentralized Federated Learning](https://mlsanity.com/paper/2205.08364/) (2022)
- [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024)
- [Homogeneous Learning: Self-Attention Decentralized Deep Learning](https://mlsanity.com/paper/2110.05290/) (2021)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning](https://mlsanity.com/paper/2205.11779/) (2022)
- [FAIR-BFL: Flexible and Incentive Redesign for Blockchain-based Federated Learning](https://mlsanity.com/paper/2206.12899/) (2022)
- [Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computing](https://mlsanity.com/paper/2302.13258/) (2023)

## Abstract (arXiv)

Federated learning has been widely studied and applied to various scenarios. In mobile computing scenarios, federated learning protects users from exposing their private data, while cooperatively training the global model for a variety of real-world applications. However, the security of federated learning is increasingly being questioned, due to the malicious clients or central servers' constant attack to the global model or user privacy data. To address these security issues, we proposed a decentralized federated learning framework based on blockchain, i.e., a Blockchain-based Federated Learning framework with Committee consensus (BFLC). The framework uses blockchain for the global model storage and the local model update exchange. To enable the proposed BFLC, we also devised an innovative committee consensus mechanism, which can effectively reduce the amount of consensus computing and reduce malicious attacks. We then discussed the scalability of BFLC, including theoretical security, storage optimization, and incentives. Finally, we performed experiments using real-world datasets to verify the effectiveness of the BFLC framework.