论文标题
使用区块链以完整性保证来利用以中心的数据联合学习
Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance
论文作者
论文摘要
机器学习能力已成为跨行业，应用和领域各种解决方案的重要组成部分。许多组织试图利用其在业务服务中基于AI的解决方案，以释放提高效率并提高生产率。但是，如果缺乏用于AI模型培训，可伸缩性和维护的质量数据，可能会出现问题。我们建议通过公共区块链和智能合约利用以数据为中心的联邦学习体系结构来克服这一重大问题。我们提出的解决方案提供了一个虚拟公共市场，开发人员，数据科学家和AI工程师可以发布其模型，并协作创建和访问培训的质量数据。我们通过激励机制增强了数据质量和完整性，从而奖励有助于数据贡献和验证。与所提出的框架相结合的那些人只有一个用户模拟培训数据集，平均每天100个输入，模型的准确性约为4 \％。
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\%.
