Distributed Neural Network Gesture Feedback
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Solution Overview
Problem
Current systems lack integration of gesture recognition with distributed neural networks, limiting their functionality and application in human-to-machine interaction.
Innovation Solution
A distributed neural network system where intelligent devices receive gesture feedback, generate outputs using neural network models, label input vectors based on user ratings, and update weights to improve model performance, with a server calculating a weighted average of weight differences to update the global neural network model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If gesture recognition is integrated with distributed neural networks, then functionality and application in human-to-machine interaction are enhanced, but system complexity increases
Solution Approach 1:
The system divides the neural network into distributed segments across multiple intelligent devices, with each device running its own neural network model. This segmentation allows gesture recognition functionality to be distributed rather than centralized, enhancing versatility while managing complexity through modular architecture.
Solution Approach 2:
The intelligent devices are designed to perform multiple functions: they serve as both gesture recognition endpoints and neural network training nodes. This multi-functionality consolidates the system by having devices perform both interaction and model improvement roles, reducing the need for separate specialized components.
2Measurement precision
If user feedback is collected and used to train neural network models, then model performance and customization are improved, but data traffic and privacy risks increase
Solution Approach 1:
The system implements local training where each intelligent device trains its own neural network model using locally collected gesture feedback. This local quality approach keeps sensitive gesture data on-device rather than transmitting it centrally, improving model performance through continuous local adaptation while preserving privacy by minimizing data transmission.
Solution Approach 2:
Instead of transmitting original gesture data, the system transmits copies in the form of weight differences or model updates. This copying approach allows the server to improve the global model without accessing actual gesture information, maintaining privacy while still enabling model refinement through distributed feedback.
3Adaptability or versatility
If multiple intelligent devices independently train their neural network models, then customization and adaptability are enhanced, but coordination and model consistency become more difficult
Solution Approach 1:
The system implements a feedback loop where each device's training results (weight differences) are transmitted to the server, which aggregates them to update the global neural network model. This feedback mechanism coordinates the distributed training efforts, ensuring that individual customizations contribute to an improved global model that maintains consistency across all devices.
Solution Approach 2:
The server merges the weight differences from multiple intelligent devices to create an updated global model. This merging process combines the benefits of independent local training with centralized coordination, allowing customization at the local level while maintaining model consistency through aggregation at the server level.
Data Source
AI summary
A method for operating a distributed neural network having a plurality of intelligent devices and a server includes: generating, by a first intelligent device of the plurality of intelligent devices, a first output using a first neural network model running on the first intelligent device and using a first input vector to the first neural network model; outputting, by the first intelligent device, the first output; receiving, by the first intelligent device, a gesture feedback on the first output from a user; determining, by the first intelligent device, a user rating of the first output from the gesture feedback; labeling, by the first intelligent device, the first input vector with a first label in accordance with the user rating; and training, by the first intelligent device, the first neural network model using the first input vector and the first label.