Hybrid ML Model Deployment to Mobile Edge Devices
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Solution Overview
Problem
Conventional centralized machine learning approaches face challenges such as high latency, bandwidth constraints, and privacy concerns in distributed edge networks, which are not effectively addressed by existing distributed machine learning techniques like Federated Learning and Gossip Learning.
Innovation Solution
A hybrid system that combines Federated Learning and Gossip Learning, utilizing a central server to receive geolocation data from mobile edge devices, index them into sub-regions using a quad-tree data structure, and rank machine learning models based on performance metrics using heapsort data structures, enabling continuous clustered training and centralized orchestration of model deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized machine learning approaches are used, then model training can be coordinated centrally, but latency increases and bandwidth constraints are exacerbated
Solution Approach 1:
The system segments the centralized training process into distributed sub-regions using quad-tree partitioning. Each sub-region performs local model training independently, then shares results with neighboring sub-regions. This segmentation reduces central coordination latency while maintaining training quality through the gossip-based information exchange mechanism.
Solution Approach 2:
The patent introduces a spatial dimension to model training by organizing devices into geographic sub-regions. Instead of a single centralized training loop, models are trained across multiple spatial partitions simultaneously, with information flowing through the spatial hierarchy of the quad-tree structure, thereby reducing temporal latency.
2Loss of energy
If distributed machine learning techniques like Federated Learning are used, then bandwidth consumption is reduced, but model performance and adaptability to local conditions decrease
Solution Approach 1:
The system implements local quality by training specialized models for each sub-region based on local data characteristics. Each sub-region develops models optimized for its specific conditions (e.g., urban vs. rural, different weather patterns), then shares these localized models with neighboring regions through gossip learning, achieving both local adaptability and broader performance.
Solution Approach 2:
The patent merges Federated Learning's bandwidth efficiency with Gossip Learning's performance benefits. Models are trained locally in each sub-region (Federated approach), then merged with neighboring sub-region models through gossip-based exchange, combining the advantages of both approaches while mitigating their individual weaknesses.
3Adaptability or versatility
If models are frequently updated across mobile devices, then model performance adapts to changing conditions, but bandwidth requirements and deployment complexity increase
Solution Approach 1:
The system performs preliminary model training and validation within each sub-region before deployment. Models are pre-adapted to local conditions and pre-validated for performance, then deployed to devices in that sub-region. This preliminary action reduces the need for frequent updates and simplifies deployment complexity.
Solution Approach 2:
The patent implements dynamic model serving where the central server actively monitors performance metrics and adaptively updates models based on changing conditions. The system dynamically adjusts which models are deployed to which sub-regions, creating a flexible deployment architecture that adapts to changing environmental conditions without requiring complex manual intervention.
4Measurement precision
If geolocation-based model selection is implemented, then models can be optimized for specific regions, but system complexity and computational overhead increase
Solution Approach 1:
The system uses asymmetric complexity distribution where the central server handles the complex quad-tree construction and model ranking operations, while edge devices perform simpler local model selection based on their geolocation. This asymmetric division of labor achieves precise geolocation-based model selection without requiring complex processing at each edge device.
Data Source
AI summary
Techniques are disclosed for hybrid machine learning model training and deployment to mobile edge devices. An example system includes a processor communicatively coupled to a memory and configured to execute instructions. Example instructions include receiving geolocation data from mobile edge devices; using the geolocation data to index the mobile edge devices into sub-regions; ranking associated machine learning (ML) models in the sub-regions based on performance metrics; and using a current geolocation of a particular mobile edge device within a given sub-region to serve a selected ML model to the particular device, wherein the ML model to serve is selected based on the performance metrics of the selected ML model within the given sub-region.