Edge Machine Learning Inference for Distributed Data Processing

The patent segments the centralized data processing architecture into distributed edge computing nodes. Each edge device performs local machine learning inference, dividing the processing workload across multiple locations rather than concentrating all data transmission to a single centralized server. This reduces network bandwidth consumption and transmission time while maintaining comprehensive analysis capability.

The patent introduces a new dimension of processing by deploying machine learning models at the edge devices themselves, rather than only at centralized servers. This spatial dimensionality change allows simultaneous local inference and centralized training, reducing the need for continuous data transmission while preserving analytical depth.

The patent performs model training and updates in advance at centralized servers using accumulated data, then deploys the trained models to edge devices. This preliminary action separates the computationally intensive training phase from the inference phase, allowing frequent model improvements without requiring continuous high-energy computation at resource-constrained edge devices.

The patent introduces a model management intermediary layer that handles model training, validation, and deployment. This intermediary coordinates between data collection, model training at centralized servers, and model distribution to edge devices, optimizing the balance between model accuracy improvements and computational energy consumption across the distributed system.