AI Model Clustering for Low-Overhead Global Updates
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Solution Overview
Problem
Existing AI systems face inefficiencies due to separate development and operation, leading to data silos and resource-intensive maintenance, which can be addressed by integrating a Machine Learning Operations (MLOps) platform for efficient management of AI models.
Innovation Solution
A method for managing local and global models on an AI platform using encoded reference information and parameter information from local servers, facilitated by a central server and local servers, with clustering based on similarity and federated learning to optimize resource use and reduce unnecessary parameter transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all local model parameters are transmitted to the central server for global model updates, then model accuracy can be improved through comprehensive data aggregation, but network resource consumption and communication costs increase significantly
Solution Approach 1:
The patent extracts only the essential parameter information needed for global model updates rather than transmitting all local model parameters. The central server selectively obtains specific parameter data from local servers, reducing communication overhead while maintaining model update effectiveness.
Solution Approach 2:
The patent implements partial action by transmitting only a subset of parameter information from local models to the central server. This selective transmission approach reduces network resource consumption while still enabling effective global model updates through the obtained parameter data.
2Manufacturing precision
If local models are trained independently without clustering, then each model can be optimized for its specific local data, but the time required for global model updates increases due to processing all models uniformly
Solution Approach 1:
The patent segments local models into different clusters based on their parameter characteristics and data distribution. This segmentation allows the central server to process and update models in groups rather than uniformly, reducing the overall time required for global model updates while maintaining local model optimization.
Solution Approach 2:
The patent changes the processing parameters by applying different update strategies to different model clusters based on their characteristics. This parameter-based differentiation enables faster global model updates by avoiding uniform processing of all models, thus reducing update time while preserving local optimization.
3Productivity
If an MLOps platform is implemented to manage AI model development and operation, then resource efficiency and maintenance costs are reduced, but system complexity and implementation overhead increase
Solution Approach 1:
The patent implements a centralized server that performs multiple functions including model parameter aggregation, clustering analysis, global model updates, and result distribution. This multi-functional approach consolidates various MLOps operations into a single system, improving resource efficiency while managing complexity through unified control.
Solution Approach 2:
The patent introduces a centralized server as an intermediary between local servers and the global model management system. This intermediary coordinates model updates, manages parameter transmission, and orchestrates clustering operations, thereby improving overall system efficiency while abstracting the complexity of distributed model management.
4Loss of energy
If encoded reference information is obtained and used for model clustering, then communication efficiency is improved by reducing parameter transmission, but additional processing steps and computational overhead are introduced
Solution Approach 1:
The patent performs preliminary encoding of reference information from local model parameters before the actual model update process. This preliminary action creates a compressed representation that reduces subsequent communication requirements, and the encoding step is performed once during the update cycle rather than repeatedly.
Solution Approach 2:
The patent creates encoded copies of reference information from local model parameters that can be transmitted and processed more efficiently. These encoded representations serve as sufficient proxies for the original parameter data, reducing communication costs while enabling the necessary clustering and update operations.
Data Source
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AI summary
Disclosed is a method for managing a local model and a global model on an AI platform, the method performed by one or more processors of a computing device according to an exemplary embodiment of the present disclosure. the method may include: obtaining encoded reference information, generated by encoding task information or data type information related to local training performed by one or more local servers; obtaining at least a part of parameter information of one or more local models locally trained by the one or more local servers; and updating the global model based on the encoded reference information and the at least a part of the parameter information of the one or more local models.