Aerospace Edge ML Model Distribution for Centralized Updates
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Solution Overview
Problem
The existing deployment, monitoring, and updating of machine learning models in complex systems, and the central device is a set of examples, and the central device is a set of devices, and the central device is arranged to communicate with the plurality of peripheral devices, and the central device is arranged to receive information describing one or more machine learning models from an external source and distribute said one or more machine learning models to the plurality of peripheral devices, and the central device is arranged to receive information describing one or more machine learning models from an external source and distribute said one or more machine learning models to the plurality of peripheral devices, and the central device is arranged to distribute said one or more machine learning models to the plurality of peripheral devices; and wherein each peripheral device is arranged to obtain input data, and the central device is arranged to distribute said one or more machine learning models to the plurality of peripheral devices; and wherein each peripheral device is arranged to obtain input data, apply a machine learning model of the one or more machine learning models to the input data to produce output data, and send said output data to the central device.
Innovation Solution
A central device coordinates the distribution of machine learning models to peripheral devices in aerospace systems, enabling efficient deployment, maintenance, and monitoring of machine learning models without extensive manual intervention, particularly in complex systems with numerous devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine learning models are deployed at edge devices in aerospace systems, then processing speed and insight timeliness are improved, but device complexity and management difficulty increase
Solution Approach 1:
A central device is introduced as an intermediary between external sources and peripheral edge devices. This central device receives machine learning model information from external sources and distributes it to multiple peripheral devices, simplifying the overall system architecture and management while enabling distributed processing speed benefits
2Reliability
If machine learning models are updated frequently to improve performance, then model accuracy is improved, but communication bandwidth and power consumption increase
Solution Approach 1:
The central device acts as an intermediary that manages model updates efficiently. It receives updated model information from external sources and distributes it to peripheral devices, consolidating communication and reducing the total bandwidth and power consumption compared to direct peer-to-peer updates
3Adaptability or versatility
If machine learning models are distributed to multiple peripheral devices, then system versatility is improved, but deployment complexity increases
Solution Approach 1:
The central device serves as a deployment intermediary that receives machine learning model information from external sources and automatically distributes it to multiple peripheral devices. This simplifies the deployment process and enhances system versatility by enabling centralized management of models across diverse edge devices
Data Source
AI summary
An aerospace system 100 is provided, comprising a plurality of peripheral devices 102 and a central device 104 operable to communicate with the plurality of peripheral devices. The central device is arranged to receive information describing one or more machine learning models from an external source 106 and distribute said one or more machine learning models to the plurality of peripheral devices. Each peripheral device is arranged to obtain input data, apply a machine learning model of the one or more machine learning models to the input data to produce output data, and send said output data to the central device.