Decentralized ML Model Trading Platform
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Solution Overview
Problem
Current machine learning prediction systems face challenges in acquiring and utilizing diverse datasets and models due to privacy issues, data ownership complexities, and inadequate collaboration mechanisms, leading to inefficient prediction processes and slow data acquisition.
Innovation Solution
A collaborative prediction platform that enables peer-to-peer trading of private datasets and models among participants, using dynamic pricing and reputation scores to facilitate knowledge sharing and improve prediction accuracy, allowing each participant to enhance their models and KPIs over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If participants share private data and models centrally, then prediction accuracy improves, but data privacy and ownership security deteriorate
Solution Approach 1:
The patent introduces a decentralized marketplace platform as an intermediary that enables participants to trade data and models without direct exposure. The platform uses smart contracts and automated scoring mechanisms to mediate transactions, allowing prediction accuracy improvement through diverse data aggregation while maintaining privacy through indirect, protocol-governed interactions rather than direct data sharing.
Solution Approach 2:
The system creates and trades copies of models and data insights rather than original private data. Participants can share model predictions, aggregated insights, and synthesized knowledge products without exposing underlying proprietary datasets, enabling accuracy improvement through knowledge propagation while preserving source data privacy and ownership.
2Measurement precision
If diverse datasets are acquired from multiple sources, then prediction accuracy improves, but data acquisition time increases
Solution Approach 1:
The system pre-trains and validates models on diverse datasets before they enter the marketplace. Data quality assessment, compatibility verification, and initial model training are performed in advance, so when participants acquire data or models through the platform, the integration time is significantly reduced compared to traditional ad-hoc data acquisition processes.
Solution Approach 2:
The marketplace platform serves multiple functions simultaneously: data discovery, quality assessment, model training, validation, and trading. This multi-functional integration consolidates what would otherwise be separate sequential processes into a unified workflow, reducing overall data acquisition time while maintaining access to diverse datasets.
3Ease of operation
If collaborative mechanisms are simplified, then ease of operation improves, but prediction accuracy deteriorates
Solution Approach 1:
The system implements automated self-service mechanisms including algorithmic trade score calculation, automatic model validation, and decentralized trading execution. These automated processes handle complex collaborative tasks without requiring manual coordination, maintaining operational simplicity for participants while enabling sophisticated multi-model ensemble methods that improve prediction accuracy.
Solution Approach 2:
The platform dynamically adjusts collaboration parameters such as trade scores, model weights, and data valuation metrics based on performance feedback and market conditions. This automated parameter optimization enables complex collaborative strategies to emerge from simple participant actions, maintaining ease of operation while achieving high prediction accuracy through adaptive ensemble methods.
Data Source
AI summary
A computer-implemented method includes publishing a prediction task and KPI information. The method further includes receiving, at a certain time, a plurality of prediction results, each of the plurality of prediction results being produced by one of a plurality of participants with one of a private model and private data association with the one of the plurality of participants. The method further includes calculating, for each of the plurality of participants, a trade score based on the KPI information and the plurality of prediction results for each of the plurality of participants. The method further includes determining an acquirement score for the one of the private model and the private data associated with each of the plurality of participants using the corresponding trade score. The method further includes publishing the acquirement score for trading the one of the private model and the private data among the plurality of participants.