Inference Model Generalization in Distributed Data Networks
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Solution Overview
Problem
In large distributed environments, managing the distribution of inference models across numerous data collectors leads to excessive computational overhead and inaccurate predictions due to the need for individual models, while also increasing communication bandwidth and power consumption through frequent data transmissions.
Innovation Solution
A system that uses a data aggregator to create a similarity graph, group nodes based on data similarity, and train inference models for groups of nodes rather than individual nodes, reducing the number of models needed and optimizing data transmission by discarding edges with low similarity thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual inference models are trained for each data collector, then prediction accuracy is improved, but computational overhead and device complexity increase excessively
Solution Approach 1:
The patent merges multiple individual inference models into a single centralized inference model that processes data from all data collectors. This consolidation maintains prediction accuracy by using aggregated training data from all collectors while significantly reducing computational overhead and device complexity at individual nodes, as the heavy inference computation is centralized rather than distributed across all devices
Solution Approach 2:
The patent segments the inference process into two distinct phases: training and execution. The training phase is centralized at the server where a single model is trained on aggregated data from all collectors, while the execution phase distributes lightweight model inference to individual data collectors. This segmentation allows accurate predictions without requiring each collector to maintain a full individual model
2Adaptability or versatility
If individual inference models are distributed to each data collector, then local prediction capability is improved, but communication bandwidth and power consumption increase due to frequent data transmissions
Solution Approach 1:
The patent merges the training function centrally while keeping inference capability distributed. A single centralized model is trained on aggregated data from all collectors, then this unified model is distributed to collectors for local inference. This approach maintains local prediction capability while reducing power consumption by eliminating the need for each collector to independently train and update its own model, thereby reducing communication frequency and energy expenditure
3Adaptability or versatility
If individual inference models are used for each data collector, then model specificity is improved, but the number of models and system complexity increase
Solution Approach 1:
The patent merges multiple individual models into a single centralized inference model that serves all data collectors. This unified model is trained on aggregated data from all collectors, providing specificity through comprehensive training data while reducing the total number of models from N individual models to just one centralized model, thereby decreasing system complexity
4Measurement precision
If all data is transmitted frequently for model training, then model accuracy is improved, but communication bandwidth consumption increases
Solution Approach 1:
The patent extracts and centralizes the data aggregation and model training functions at the server, removing the need for frequent bidirectional data transmissions between collectors and individual model updates. Raw data is transmitted once to the server for centralized training, and only the final trained model parameters are distributed back, significantly reducing communication bandwidth consumption while maintaining model accuracy through comprehensive training data
Data Source
AI summary
Methods and systems for managing generalization of inference models throughout a distributed environment are disclosed. To manage generalization of inference models, a system may include a data aggregator and one or more data collectors. The data aggregator may obtain a similarity graph in order to determine the relationship between data obtained by one or more data collectors. The similarity graph may be used to obtain grouping for the data collectors. The data aggregator may train inference models to facilitate data collection by the data collectors included in the grouping.