Secure Distributed AI Model Clustering via NFT Authentication
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Solution Overview
Problem
The complexity of AI-ML model development and execution requires extensive infrastructure, necessitating a secure decentralized infrastructure provisioning system for distributed environments to manage and host machine learning models efficiently.
Innovation Solution
A secure distributed machine learning system using distributed ledger technology, which identifies and clusters AI-ML model components based on performance output requirements, distributes them across a network, and uses non-fungible tokens for authentication and aggregation, optimizing resource utilization and ensuring model integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If AI-ML model components are distributed across multiple host devices, then resource utilization is optimized and infrastructure complexity is reduced, but model integrity and security may be compromised
Solution Approach 1:
The AI-ML model is divided into multiple components that are distributed across different host devices. Each component is independently managed and tracked through NFTs, allowing the system to optimize resource utilization across the network while maintaining overall model integrity through cryptographic verification of each segment.
Solution Approach 2:
A distributed ledger system with NFT-based verification acts as an intermediary between the distributed model components and the aggregation process. This intermediary ensures that each component's integrity is verified and authenticated before being combined, preventing tampering and ensuring model reliability without requiring centralized control.
2Reliability
If NFT verification is implemented for each AI-ML model component, then model authenticity and tamper-proofing are ensured, but processing time and computational overhead increase
Solution Approach 1:
NFT verification data and model component metadata are prepared and stored in advance on the distributed ledger before the actual model execution. This preliminary action allows the verification process to occur efficiently during model aggregation, reducing real-time processing overhead while maintaining strong authenticity checks.
3Productivity
If AI-ML model components are clustered based on performance output requirements, then resource optimization is improved, but system complexity and coordination overhead increase
Solution Approach 1:
The system employs automated clustering algorithms that independently analyze performance requirements and automatically assign model components to appropriate host devices based on available resources and performance characteristics. This self-service approach optimizes resource utilization without requiring manual intervention or complex centralized coordination, reducing operational complexity while maintaining high productivity.
Data Source
AI summary
Embodiments of the invention are directed to systems, methods, and computer program products for a secure distributed machine learning activity system using distributed ledger technology. The invention comprises an AI-ML model development de-centralized system that provides a secure mechanism which give secure provision to entities to develop AI-ML models in a distributed environment. The system allows the release of AI-ML model components, programs, binary files, and the like in distributed host system infrastructure in secure manner. Therefore, optimizing computer resources for various requirements, such as image processing or the like that may be performed on other secure mechanisms across the secure distributed network. In this way, distributing the AI-ML model across multiple devices across the secure distributed network.