Distributed AI Ensemble Fusion for Heterogeneous Node Training
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Solution Overview
Problem
Current methods for training artificial intelligence models in distributed environments face challenges due to large-scale data transmission costs, data integrity issues, regulatory constraints, uneven data distribution, and lack of trust among corporations, which hinder the creation of accurate AI models across heterogeneous systems.
Innovation Solution
A distributed learning approach using an ensemble-based fusion method where each system node determines local data statistics, shares model parameters, and constructs mini-batches to balance class distribution, allowing for local training and fusion of AI models without transferring all data, thus reducing data transmission and ensuring model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all data is transmitted to a centralized location for training AI models, then model accuracy can be improved, but data transmission costs and network bandwidth consumption increase significantly
Solution Approach 1:
The patent segments the centralized training process into distributed local training at multiple system nodes. Each node trains AI models locally using its own data, eliminating the need to transmit all data to a centralized location. The segmented models are then fused to create the final accurate model, resolving the contradiction between model accuracy and transmission costs.
Solution Approach 2:
Instead of transmitting original data, the patent transmits copied model parameters and statistics between nodes. Each node creates local copies of AI models, trains them independently, and shares only the model copies rather than the underlying data, significantly reducing transmission costs while maintaining model accuracy through fusion.
2Measurement precision
If data is shared across multiple corporations for AI training, then model performance can be improved, but data integrity and regulatory compliance issues arise
Solution Approach 1:
The patent segments data ownership and processing across independent system nodes representing different corporations. Each corporation keeps its data locally at its own node, maintaining data integrity and regulatory compliance. The segmentation allows collaborative model training without actual data sharing, eliminating data integrity concerns while improving model performance through ensemble fusion.
Solution Approach 2:
The patent introduces an intermediary fusion process that combines models from multiple independent nodes without requiring direct data sharing. The intermediary fusion mechanism aggregates local models into a global model, enabling collaborative performance improvement while preserving data integrity and compliance at each node.
3Adaptability or versatility
If data is distributed across heterogeneous systems, then system scalability and autonomy are improved, but uneven data distribution and lack of coordination reduce model accuracy
Solution Approach 1:
The patent implements a universal ensemble fusion framework that works across heterogeneous system nodes with different data distributions and architectures. The fusion mechanism provides multi-functionality by handling diverse model types and data characteristics uniformly, enabling scalable integration of heterogeneous systems while maintaining model accuracy through coordinated ensemble training.
Solution Approach 2:
The patent employs feedback mechanisms where system nodes exchange model statistics and performance metrics to coordinate training. The feedback loop allows heterogeneous nodes to adjust their local training processes based on global ensemble performance, improving model accuracy while preserving system scalability and autonomy.
4Loss of energy
If local data is used for training at each system node, then data transmission costs are reduced, but class distribution imbalances reduce model accuracy
Solution Approach 1:
The patent merges multiple locally trained models into a fused ensemble model that compensates for individual node class distribution imbalances. Each node trains locally using its own data (reducing transmission costs), and the merging process combines these models to create a globally accurate model that overcomes local data limitations, resolving the contradiction between transmission costs and model accuracy.
Data Source
AI summary
Embodiments of the invention are directed to a computer-implemented method of distributed learning using a fusion-based approach. The method includes determining data statistics at each system node of a plurality of system nodes, wherein each system node respectively comprises an artificial intelligence model. The method further includes determining a set of control and coordination instructions for training each artificial intelligence model at each system node of the plurality of system nodes. The method further includes directing an exchange of data between the plurality of system nodes based on the data statistics of each system node of the plurality of system nodes. The method further includes fusing trained artificial intelligence models from the plurality of system nodes into a fused artificial intelligence model, wherein the trained artificial intelligence models are trained using the set of control and coordination instructions.