# Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus

arXiv: https://arxiv.org/abs/2101.03300 · HTML: https://mlsanity.com/paper/2101.03300/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 9 Jan 2021.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Mitigating Sybils in Federated Learning Poisoning](https://mlsanity.com/paper/1808.04866/) (2018)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [Wireless Network Intelligence at the Edge](https://mlsanity.com/paper/1812.02858/) (2018)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus](https://mlsanity.com/paper/2004.00773/) (2020)
- [Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications](https://mlsanity.com/paper/2008.02608/) (2020)
- [BAFFLE : Blockchain Based Aggregator Free Federated Learning](https://mlsanity.com/paper/1909.07452/) (2019)
- [A Secure Federated Learning Framework for 5G Networks](https://mlsanity.com/paper/2005.05752/) (2020)
- [When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm](https://mlsanity.com/paper/2009.09338/) (2020)
- [GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning](https://mlsanity.com/paper/1909.00047/) (2019)
- [Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm](https://mlsanity.com/paper/1912.11745/) (2019)
- [A Federated Learning Framework for Healthcare IoT devices](https://mlsanity.com/paper/2005.05083/) (2020)
- [Distilling On-Device Intelligence at the Network Edge](https://mlsanity.com/paper/1908.05895/) (2019)

## Built on by (most cited first, 14 of 14)

- [Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions](https://mlsanity.com/paper/2303.02213/) (2023)
- [Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV](https://mlsanity.com/paper/2407.02969/) (2024)
- [Blockchained Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2305.04513/) (2023)
- [Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation](https://mlsanity.com/paper/2306.11297/) (2023)
- [SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation](https://mlsanity.com/paper/2201.07730/) (2022)
- [BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model](https://mlsanity.com/paper/2310.13403/) (2023)
- [Towards Secure and Private AI: A Framework for Decentralized Inference](https://mlsanity.com/paper/2407.19401/) (2024)
- [Blockchain-Enabled Federated Learning](https://mlsanity.com/paper/2508.06406/) (2025)
- [Blockchain-Enabled Federated Learning Approach for Vehicular Networks](https://mlsanity.com/paper/2311.06372/) (2023)
- [FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication](https://mlsanity.com/paper/2312.00035/) (2023)
- [A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain](https://mlsanity.com/paper/2407.07921/) (2024)
- [Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation](https://mlsanity.com/paper/2407.12226/) (2024)
- [Decentralized Time Series Classification with ROCKET Features](https://mlsanity.com/paper/2504.17617/) (2025)
- [DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning](https://mlsanity.com/paper/2512.12022/) (2025)

## Abstract (arXiv)

Federated learning (FL) is a promising distributed learning solution that only exchanges model parameters without revealing raw data. However, the centralized architecture of FL is vulnerable to the single point of failure. In addition, FL does not examine the legitimacy of local models, so even a small fraction of malicious devices can disrupt global training. To resolve these robustness issues of FL, in this paper, we propose a blockchain-based decentralized FL framework, termed VBFL, by exploiting two mechanisms in a blockchained architecture. First, we introduced a novel decentralized validation mechanism such that the legitimacy of local model updates is examined by individual validators. Second, we designed a dedicated proof-of-stake consensus mechanism where stake is more frequently rewarded to honest devices, which protects the legitimate local model updates by increasing their chances of dictating the blocks appended to the blockchain. Together, these solutions promote more federation within legitimate devices, enabling robust FL. Our emulation results of the MNIST classification corroborate that with 15% of malicious devices, VBFL achieves 87% accuracy, which is 7.4x higher than Vanilla FL.