Distributed Marketplace Ensemble Training for ML Model Selection
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Solution Overview
Problem
Training machine learning models over arbitrary datasets is time-consuming and resource-intensive, and existing methods fail to effectively compare and remunerate individual models based on their quality and performance in a distributed computing environment like a blockchain.
Innovation Solution
The solution involves ensemble training in a distributed marketplace, where multiple machine learning models compete to achieve a performance threshold on ensemble training data, with mechanisms to coordinate model training, optimize ensemble performance, and fairly remunerate model producers based on their relative performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple machine learning models are trained and competed in a distributed marketplace, then model quality and performance can be improved, but training time and computational resources increase
Solution Approach 1:
The patent divides the model training process into independent segments that can be executed in parallel across multiple distributed computing nodes. Each node trains individual models or model components separately, then results are aggregated. This segmentation enables simultaneous training of multiple models without sequential delays, improving throughput while maintaining performance quality.
Solution Approach 2:
The system performs preliminary actions by pre-processing training data and pre-configuring model architectures before the actual training competition begins. Training datasets are pre-split into batches, and model parameters are pre-initialized, allowing the competitive training phase to start immediately without setup delays, thus reducing overall training time while maintaining model quality.
2Measurement precision
If multiple machine learning models are trained and competed in a distributed marketplace, then model quality and performance can be improved, but computational resources and complexity increase
Solution Approach 1:
The patent introduces intermediary components including a centralized marketplace platform and coordination layer that manage the competition between multiple models. This intermediary handles model registration, performance evaluation, and result aggregation, simplifying the complexity by providing a standardized interface between distributed training nodes and the final model selection process.
Solution Approach 2:
The system employs universal model architectures and standardized training pipelines that can handle multiple model types and tasks. The distributed marketplace infrastructure is designed to be multi-functional, supporting various model formats, evaluation metrics, and aggregation methods, thereby reducing complexity through standardization while maintaining the ability to train and compare diverse high-performance models.
3Reliability
If ensemble training is performed on a blockchain, then fair compensation for model producers can be ensured, but transaction overhead and processing time increase
Solution Approach 1:
The patent extracts the compensation and payment verification processes from the main model training and evaluation workflow. Smart contracts on the blockchain handle compensation automatically based on pre-defined performance criteria, separating the trustless payment mechanism from the computational training process. This extraction ensures fair compensation through immutable blockchain records while minimizing transaction overhead by batching payments and using efficient smart contract logic.
Data Source
AI summary
Embodiments for ensemble training in a distributed marketplace in a computing environment. One or more ensemble machine learning models may be provided from a plurality of machine learning models competing within the distributed marketplace that achieve a performance on ensemble training data equal to or greater than a selected performance threshold, wherein the distributed marketplace is a blockchain.