Leveraging Centric Data Federated Learning Using Blockchain For
Integrity Assurance
- URL: http://arxiv.org/abs/2206.04731v1
- Date: Thu, 9 Jun 2022 19:06:05 GMT
- Title: Leveraging Centric Data Federated Learning Using Blockchain For
Integrity Assurance
- Authors: Riadh Ben Chaabene, Darine Amayed and Mohamed Cheriet
- Abstract summary: We propose a data-centric federated learning architecture leveraged by a public blockchain and smart contracts.
Our proposed solution provides a virtual public marketplace where developers, data scientists, and AI-engineer can publish their models.
We enhance data quality and integrity through an incentive mechanism that rewards contributors for data contribution and verification.
- Score: 14.347917009290814
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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\%.
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