Inference Model Optimization for Distributed Data Aggregation
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Solution Overview
Problem
Existing data collection systems face challenges in efficiently managing data aggregation in distributed environments, leading to excessive computing resource consumption and bandwidth usage due to the need for accurate inference models.
Innovation Solution
A system that optimizes inference model distribution by determining the least computationally costly model meeting accuracy thresholds, using a data aggregator to simulate operations and select twin inference models for reduced data transmission, thereby conserving resources and bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If accurate inference models are used for data aggregation, then data accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The system changes the parameter of inference model complexity by selecting from multiple pre-trained models with different computational requirements. The data aggregator evaluates accuracy metrics for each model and selects the one that achieves the required accuracy threshold with minimal computing resource consumption, thus resolving the contradiction between data accuracy and computing resource usage.
Solution Approach 2:
Inference models are pre-trained offline with various accuracy levels and computational complexities before deployment. This preliminary action allows the system to have ready-to-use models that can be selected based on current resource constraints and accuracy requirements, avoiding the need to train complex models on-demand and reducing real-time computing resource consumption.
2Measurement precision
If full data is transmitted to the data aggregator, then data aggregation accuracy is improved, but network bandwidth usage increases
Solution Approach 1:
The system extracts only the essential data features that are necessary for accurate aggregation, rather than transmitting complete raw datasets. The data collector uses the selected inference model to process data locally and extract only the critical information needed by the data aggregator, thereby maintaining aggregation accuracy while significantly reducing network bandwidth consumption.
Solution Approach 2:
The data collector creates a local copy of the inference model and uses it to generate predictions or extracted features that are then transmitted to the data aggregator. This copying approach allows the aggregator to receive processed, compact data representations that maintain accuracy while reducing transmission bandwidth requirements.
3Measurement precision
If complex inference models are deployed, then inference accuracy is improved, but device computational load increases
Solution Approach 1:
The system changes the computational complexity parameter of the inference model by selecting from a range of pre-trained models with varying architectures and sizes. The data collector evaluates the accuracy performance of different model options and selects the least complex model that meets the required accuracy threshold, thereby reducing device computational load while maintaining inference accuracy.
Data Source
AI summary
Methods and systems for managing distribution of inference models throughout a distributed system are disclosed. To manage distribution of inference models, a system may include a data aggregator and one or more data collectors. The data aggregator may obtain a threshold, the threshold indicating an acceptable inference error rate for an inference model. The data aggregator may obtain an inference model based on the threshold by training an inference model, performing a lookup in an inference model lookup table, or via other methods. The data aggregator may optimize the inference model to determine the minimum quantity of computing resources consumed by an inference model in order to generate inferences accurate within the threshold. In order to do so, the data aggregator may simulate the operation of more computationally-costly inference models and less computationally-costly inference models.