Distributed Learning Model for Reducing Bandwidth and Load
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for simulating computer-generated environments face challenges in reducing computational efforts on both server and client devices while minimizing bandwidth variability between them, which can degrade user experience and increase network and device bandwidth usage.
Innovation Solution
A distributed learning and activation model is implemented using a network of computing nodes, where machine learning models on server and client devices exchange information in near real-time for mutual learning, with reversible compression to reduce computational load and bandwidth usage, allowing for efficient rendering of content without relying on continuous server-client communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If a server streams information to multiple client devices to render content, then the rendering load on client devices is reduced, but network bandwidth usage increases and server computational effort decreases
Solution Approach 1:
The patent divides the rendering workload into segments: the server performs initial rendering and sends key frame data to clients, while clients perform local rendering of supplementary content. This segmentation reduces network bandwidth usage by avoiding transmission of complete high-resolution frames while still reducing client device load compared to pure client-side rendering.
Solution Approach 2:
The patent introduces an intermediary compression and transmission system that processes rendering data between server and clients. The server renders content, compresses it into essential information packets, and transmits these to clients which then reconstruct and render the final output, mediating the bandwidth-load tradeoff.
2Productivity
If a server renders content once and sends information to multiple clients simultaneously, then the reuse ratio between server efforts and clients is high, but network bandwidth variability between server and clients increases
Solution Approach 1:
The patent implements dynamic adaptation where the rendering strategy adjusts based on network conditions. When network bandwidth is stable, the system uses high reuse ratio rendering; when bandwidth varies, it shifts to more client-side rendering with less server dependency, maintaining reliability while preserving productivity.
Solution Approach 2:
The system changes rendering parameters dynamically - adjusting the balance between server-rendered content and client-rendered content based on network conditions. This allows the reuse ratio and bandwidth stability to be optimized together by varying the rendering distribution parameters.
3Adaptability or versatility
If computing nodes exchange information in near real time for mutual learning, then model accuracy and adaptability improve, but computational load and communication overhead increase
Solution Approach 1:
The patent implements partial mutual learning where computing nodes exchange only essential model update information rather than complete model states. This partial action approach maintains model adaptability through selective information exchange while reducing computational load and communication overhead by transmitting only necessary updates.
4Quantity of substance
If client devices handle local changes and updates independently, then network bandwidth usage is reduced, but device complexity and synchronization challenges increase
Solution Approach 1:
The patent enables client devices to perform self-service rendering by providing them with compressed rendering data and essential model information from the server. Clients independently reconstruct and render content locally without requiring continuous server communication, reducing bandwidth usage while managing complexity through efficient data representation.
Data Source
AI summary
The present disclosure describes a system and method for communicating distributed learning and activation model for machine learning program in any environment. The system includes a plurality of computing nodes in communication with each other through a network. The computing nodes include a set of first computing nodes and a set of second computing nodes. The first computing node is configured to manage one set of machine learning models and the second computing node is configured to manage another set of machine learning models. The first computing node and second computing node are configured to provide timely information to update the model dynamically being learned. The first computing node and second computing node variably update the model. The updates are independent of network delays.