# Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance

arXiv: https://arxiv.org/abs/2206.04731 · HTML: https://mlsanity.com/paper/2206.04731/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Multi-Center Federated Learning: Clients Clustering for Better Personalization](https://mlsanity.com/paper/2108.08647/) (2021)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [BAFFLE : Blockchain Based Aggregator Free Federated Learning](https://mlsanity.com/paper/1909.07452/) (2019)
- [When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm](https://mlsanity.com/paper/2009.09338/) (2020)
- [Decentralized & Collaborative AI on Blockchain](https://mlsanity.com/paper/1907.07247/) (2019)
- [Pitfalls in Machine Learning Research: Reexamining the Development Cycle](https://mlsanity.com/paper/2011.02832/) (2020)
- [Flow-FL: Data-Driven Federated Learning for Spatio-Temporal Predictions in Multi-Robot Systems](https://mlsanity.com/paper/2010.08595/) (2020)
- [Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain](https://mlsanity.com/paper/1802.10185/) (2018)
- [IPFS and Friends: A Qualitative Comparison of Next Generation Peer-to-Peer Data Networks](https://mlsanity.com/paper/2102.12737/) (2021)
- [A comprehensive survey on smart contract construction and execution: paradigms, tools, and systems](https://mlsanity.com/paper/2008.13413/) (2020)
- [A Unified Analytical Framework for Trustable Machine Learning and Automation Running with Blockchain](https://mlsanity.com/paper/1903.08801/) (2019)

## Abstract (arXiv)

Machine learning abilities have become a vital component for various solutions across industries, applications, and sectors. Many organizations seek to leverage AI-based solutions across their business services to unlock better efficiency and increase productivity. Problems, however, can arise if there is a lack of quality data for AI-model training, scalability, and maintenance. We propose a data-centric federated learning architecture leveraged by a public blockchain and smart contracts to overcome this significant issue. Our proposed solution provides a virtual public marketplace where developers, data scientists, and AI-engineer can publish their models and collaboratively create and access quality data for training. We enhance data quality and integrity through an incentive mechanism that rewards contributors for data contribution and verification. Those combined with the proposed framework helped increase with only one user simulation the training dataset with an average of 100 input daily and the model accuracy by approximately 4%.