Blockchain Swarm Learning Data Monetization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems face challenges in managing geo-distributed, decentralized enterprise infrastructures due to scale and heterogeneity, particularly in sharing data for machine learning models while ensuring privacy and accurate reward determination, leading to issues with data quality and redundant data contributions.
Innovation Solution
The implementation of a blockchain-based framework for decentralized management and swarm learning, where edge nodes encrypt and hash data, build Merkle trees, and register Merkle roots to ensure data privacy and accuracy, with smart contracts handling reward distribution based on unique data contributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is shared across decentralized nodes for machine learning model training, then the robustness and accuracy of the model is improved, but data privacy and security are compromised
Solution Approach 1:
The patent extracts only the essential information needed for model training (gradients, model parameters) while leaving the actual sensitive data on local devices. This extraction approach allows collaborative model training without exposing raw private data, resolving the contradiction between model robustness and data privacy.
Solution Approach 2:
The patent introduces blockchain as an intermediary layer that mediates between data holders and model trainers. The blockchain verifies and records data contribution proofs without accessing the actual sensitive data, enabling trusted collaboration while maintaining privacy boundaries.
2Quantity of substance
If decentralized nodes contribute data to machine learning models, then data quantity increases, but redundant and low-quality data contributions occur
Solution Approach 1:
The patent implements a feedback mechanism where the central server evaluates data quality and provides rewards or penalties to contributing nodes. This feedback loop ensures that only high-quality, non-redundant data contributions are incentivized, maintaining data quality while accumulating sufficient data quantity.
Solution Approach 2:
The patent performs preliminary verification of data quality and uniqueness before incorporating data into the model training process. By checking data provenance and quality metrics in advance, the system prevents redundant and low-quality data from being processed, ensuring high data quality while maintaining adequate quantity.
3Adaptability or versatility
If decentralized infrastructure is managed without central coordination, then system autonomy increases, but management complexity and coordination difficulty increase
Solution Approach 1:
The patent implements a universal blockchain protocol that serves multiple functions: verifying data contributions, recording model training progress, managing reward distributions, and coordinating between nodes. This multi-functional framework simplifies coordination complexity while maintaining system autonomy.
Solution Approach 2:
The patent enables decentralized nodes to self-verify their data contributions and self-report training results to the blockchain. This self-service approach reduces the need for complex central coordination while maintaining system-wide consistency and autonomy.
Data Source
Figure 1A
Figure 1B~1C
Figure 1D
AI summary
Systems and methods are provided for leveraging blockchain technology in a swarm learning context, where nodes of a blockchain network that contribute data to training a machine learning model using their own local data can be rewarded. In order to conduct such data monetization in a fair and accurate manner, the systems and methods rely on various phases in which Merkle trees are used and corresponding Merkle roots are registered in a blockchain ledger. Moreover, any claims for a reward are challenged by peer nodes before the reward is distributed.