Twin Inference Models for Distributed Data Aggregation
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Solution Overview
Problem
Existing data collection systems face challenges in managing limited computing resources, leading to increased computing resource expenditures and reduced availability for other purposes, particularly in distributed environments where data aggregation is required with a desired level of accuracy.
Innovation Solution
The implementation of twin inference models trained on a model training device with sufficient computing resources, which are deployed based on a similarity graph to reduce data transmission and error, allowing for efficient data aggregation while minimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is aggregated from distributed data collectors with desired accuracy, then measurement precision is improved, but computing resource expenditures increase
Solution Approach 1:
The patent segments the data collection system into distributed data collectors and a central data aggregator, allowing local preprocessing at collector nodes. This segmentation enables the system to maintain high measurement precision through distributed inference while reducing the computing burden on any single node, thereby lowering overall computing resource expenditures.
Solution Approach 2:
The patent implements preliminary action by training inference models in advance and deploying them to data collectors before actual data aggregation. This allows collectors to perform local inference and filtering, reducing the volume and complexity of data transmitted to the aggregator, thus maintaining accuracy while reducing computing resource usage during operation.
2Measurement precision
If more computing resources are allocated to data aggregation, then data aggregation accuracy is improved, but availability of resources for other purposes decreases
Solution Approach 1:
By segmenting the inference workload across multiple distributed collectors rather than concentrating it at the central aggregator, the system achieves high data aggregation accuracy without requiring a single powerful computing resource. This distribution frees up computing resources at the aggregator for other purposes while maintaining productivity through parallel processing at collector nodes.
Solution Approach 2:
The patent enables self-service by equipping data collectors with deployed inference models that allow them to perform local inference and data processing autonomously. This reduces the need for centralized computing resources, improving resource availability for other purposes while maintaining aggregation accuracy through distributed intelligence.
3Use of energy by moving object
If twin inference models are deployed to reduce data transmission, then computing resource expenditures are reduced, but device complexity increases
Solution Approach 1:
The patent employs copying by deploying identical or similar inference models to multiple data collectors. This approach reduces computing resource expenditures through efficient local processing while managing device complexity by using standardized, reusable model templates that can be replicated across collectors without requiring complex custom implementations at each node.
Data Source
AI summary
Methods and systems for managing data aggregation in a distributed environment are disclosed. The data may be aggregated using twin inference models which may be used to reduce a quantity of data transmitted to aggregate the data. To obtain twin inference models, models may be trained which may consume computing resources. A computing resource cost for training the twin inference models may be estimated based on an estimated number of twin inferences models necessary to meet inference accuracy goals. A model training device that has an available quantity of computing resources sufficient to meet the computing resource cost may be obtained. The model training device may be used to train and distribute inference models for data aggregation purposes.