# When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm

arXiv: https://arxiv.org/abs/2009.09338 · HTML: https://mlsanity.com/paper/2009.09338/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 30 of 31)

- [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)
- [IronForge: An Open, Secure, Fair, Decentralized Federated Learning](https://mlsanity.com/paper/2301.04006/) (2023)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus](https://mlsanity.com/paper/2101.03300/) (2021)
- [OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning](https://mlsanity.com/paper/2107.05252/) (2021)
- [FedToken: Tokenized Incentives for Data Contribution in Federated Learning](https://mlsanity.com/paper/2209.09775/) (2022)
- [G2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering](https://mlsanity.com/paper/2306.04984/) (2023)
- [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)
- [Blockchain-based Secure Client Selection in Federated Learning](https://mlsanity.com/paper/2205.05611/) (2022)
- [Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data](https://mlsanity.com/paper/2402.00205/) (2024)
- [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024)
- [FAIR-BFL: Flexible and Incentive Redesign for Blockchain-based Federated Learning](https://mlsanity.com/paper/2206.12899/) (2022)
- [Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification](https://mlsanity.com/paper/2306.10841/) (2023)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing](https://mlsanity.com/paper/2303.13727/) (2023)
- [Analysis and Evaluation of Synchronous and Asynchronous FLchain](https://mlsanity.com/paper/2112.07938/) (2021)
- [Use of Federated Learning and Blockchain towards Securing Financial Services](https://mlsanity.com/paper/2303.12944/) (2023)
- [Swarm Learning: A Survey of Concepts, Applications, and Trends](https://mlsanity.com/paper/2405.00556/) (2024)
- [Decentralized Intelligence Network (DIN)](https://mlsanity.com/paper/2407.02461/) (2024)
- [Federated Learning and AI Regulation in the European Union: Who is Responsible? -- An Interdisciplinary Analysis](https://mlsanity.com/paper/2407.08105/) (2024)
- [A Framework for Verifiable and Auditable Federated Anomaly Detection](https://mlsanity.com/paper/2203.07802/) (2022)
- [Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance](https://mlsanity.com/paper/2206.04731/) (2022)
- [Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations](https://mlsanity.com/paper/2405.06993/) (2024)
- [Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT](https://mlsanity.com/paper/2409.15711/) (2024)
- [A new framework for prognostics in decentralized industries: Enhancing fairness, security, and transparency through Blockchain and Federated Learning](https://mlsanity.com/paper/2503.05725/) (2025)
- [Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning](https://mlsanity.com/paper/2605.08616/) (2026)
- [Skillsets on the Chain: A Blockchain-based Zero-Trust Framework for Agentic AI Networking](https://mlsanity.com/paper/2608.00104/) (2026)
- [A Lifecycle Cost Analysis of Smart-Contract-Coordinated Federated Learning Marketplaces](https://mlsanity.com/paper/2609.13170/) (2026)

## Abstract (arXiv)

Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged. By training models locally at each client and aggregating learning models at a central server, FL has the capability to avoid sharing data directly, thereby reducing privacy leakage. However, the traditional FL framework heavily relies on a single central server and may fall apart if such a server behaves maliciously. To address this single point of failure issue, this work investigates a blockchain assisted decentralized FL (BLADE-FL) framework, which can well prevent the malicious clients from poisoning the learning process, and further provides a self-motivated and reliable learning environment for clients. In detail, the model aggregation process is fully decentralized and the tasks of training for FL and mining for blockchain are integrated into each participant. In addition, we investigate the unique issues in this framework and provide analytical and experimental results to shed light on possible solutions.