Distributed Edge Models via Controlled Dropout Training
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Solution Overview
Problem
Implementing machine learning models in distributed edge environments is challenging due to complexities in algorithmic and mathematical aspects, resource consumption, and the need for frequent model regeneration with changes in use cases or environments, which differs significantly from traditional software engineering practices.
Innovation Solution
The development of consistent distributed edge models through controlled dropout model training, where subsets of sensor data streams are dropped during training to create robust models tolerant to missing input data, allowing for accurate results even with inter-sensor device communication faults, and utilizing peer-to-peer communication protocols for device-to-device model execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained using all sensor data streams, then model accuracy is improved, but the model becomes vulnerable to communication faults and missing data in distributed edge environments
Solution Approach 1:
The patent applies preliminary action by training the model in advance with artificially dropped sensor data streams to simulate communication faults. This preparatory training enables the model to learn robust feature representations that can tolerate missing data during actual deployment, resolving the contradiction between accuracy and fault tolerance
Solution Approach 2:
The patent implements beforehand cushioning by introducing data dropout during training as a protective measure against future communication failures. This anticipatory approach cushions the model against the harmful effects of missing data, allowing it to maintain accuracy even when sensor communications fail in distributed edge environments
2Productivity
If machine learning models are deployed to distributed edge devices, then processing speed and bandwidth efficiency are improved, but model consistency across devices deteriorates due to communication faults
Solution Approach 1:
The patent applies parameter changes by modifying the training regime to include random data stream dropout, which changes the model's internal parameters to be more robust. This enables the model to maintain consistent predictions across distributed devices even when communication parameters (data availability) vary, resolving the contradiction between processing speed and model consistency
3Measurement precision
If traditional centralized training is used, then model accuracy is maintained, but resource consumption and deployment complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the training process into distributed edge device training with localized sensor data. Each device trains its own model instance independently with dropout regularization, eliminating the need for complex centralized training infrastructure and reducing deployment complexity while maintaining accuracy through the robustness learned via dropout
Data Source
AI summary
Consistent distributed edge models via controlled dropout model training are described. According to some embodiments, a machine learning model is trained using multiple sensor data streams. During the training process, ones of the sensor data streams are dropped to cause the model to be generated to be robust in that it can tolerate missing input data from sensor data sources yet still maintain high accuracy. The model can be deployed to multiple sensor devices within an environment. The sensor devices generate sensor data and exchange a variety of types of data to ultimately result in a distributed, consistent model result being generated that remains accurate despite communication faults that may occur between ones of the sensor devices.