Edge Node Distributed Machine Learning Model Parameter Synchronization
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Solution Overview
Problem
Existing machine learning approaches face challenges in varying domains and incur high costs in terms of network bandwidth, energy, and user privacy, particularly when training models at edge nodes in distributed computer systems, as they require uploading data to centralized servers for training.
Innovation Solution
A distributed learning approach that performs local parameter updates at edge nodes and global synchronization to combine parameters iteratively, using a synchronization node to adjust the number of iterations based on resource availability, reducing the need to send raw data to a centralized location and maintaining privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is uploaded to centralized servers for training, then machine learning models can be trained effectively, but network bandwidth consumption increases and user privacy is compromised
Solution Approach 1:
The training process is segmented into local training at edge nodes and global synchronization at the centralized server. Each edge node performs local training iterations on its own data, and only model parameters are exchanged with the server, rather than transferring all raw data. This segmentation reduces network bandwidth consumption while maintaining training effectiveness.
Solution Approach 2:
The patent extracts only the essential model parameters from the training process, separating them from the large volume of raw training data. By transmitting only these extracted parameters between edge nodes and the centralized server, the system achieves effective model training with minimal network bandwidth consumption.
2Reliability
If data is uploaded to centralized servers for training, then machine learning models can be trained effectively, but user privacy is compromised
Solution Approach 1:
The training process is segmented so that raw user data remains localized at edge nodes and is never transmitted to the centralized server. Only aggregated model parameters are exchanged, ensuring that user privacy is preserved while model training effectiveness is maintained through collaborative learning.
Solution Approach 2:
The patent extracts only the learned model parameters from the training process, leaving the sensitive raw user data at edge nodes. This extraction approach enables effective model training while preventing exposure of user privacy information to centralized servers.
3Measurement precision
If training iterations are increased at edge nodes, then model accuracy improves, but resource consumption exceeds available budget
Solution Approach 1:
The number of training iterations at edge nodes is made dynamic rather than fixed. The system adjusts the number of iterations based on real-time resource availability and model convergence requirements, allowing model accuracy to improve while adapting to resource constraints and preventing budget exhaustion.
Solution Approach 2:
The patent dynamically changes training parameters (number of iterations, learning rate) based on resource availability and model performance. By adjusting these parameters, the system optimizes the balance between model accuracy improvement and resource consumption, ensuring training remains within the available resource budget.
4Loss of energy
If local training is performed at edge nodes, then network bandwidth is reduced, but model convergence becomes slower
Solution Approach 1:
The system implements periodic synchronization where edge nodes perform multiple local training iterations and then synchronize with the centralized server at regular intervals. This periodic exchange of model parameters maintains network bandwidth efficiency while ensuring model convergence through regular global updates that prevent divergence.
Data Source
AI summary
A training process of a machine learning model is executed at the edge node for a number of iterations to generate a model parameter based at least in part on a local dataset and a global model parameter. A resource parameter set indicative of resources available at the edge node is estimated. The model parameter and the resource parameter set are sent to a synchronization node. Updates to the global model parameter and the number of iterations are received from the synchronization node based at least in part on the model parameter and the resource parameter set of edge nodes. The training process of the machine learning model is repeated at the edge node to determine an update to the model parameter based at least in part on the local dataset and updates to the global model parameter and the number of iterations from the synchronization node.