Distributed Learning Model Synchronization with Adaptive Intervals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional machine learning techniques face challenges in achieving a balance between accuracy and time efficiency in synchronizing learning models across distributed data processing nodes due to the use of static synchronization intervals, which can lead to either overfitting or increased computational overhead.
Innovation Solution
An adaptive synchronization method where a master node adjusts synchronization intervals based on the accuracy difference between central and average learning model accuracies, using a leader election algorithm to optimize the trade-off between learning time and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static synchronization intervals are used in distributed learning, then the system structure is simple and easy to implement, but the accuracy of the final learning model deteriorates or training time increases
Solution Approach 1:
The patent transforms the static synchronization interval into a dynamic one by introducing an accuracy metric that monitors learning progress. The synchronization interval automatically adjusts based on whether the accuracy metric improves beyond a threshold, making the system adaptive to the actual learning state rather than relying on fixed predetermined intervals.
Solution Approach 2:
The patent changes the parameter of synchronization interval from a fixed value to a variable that depends on the accuracy metric. By monitoring the improvement in accuracy across synchronization intervals and comparing it to a threshold, the system dynamically modifies the interval parameter to optimize both accuracy and training time.
2Manufacturing precision
If frequent synchronization is performed, then the accuracy of the final learning model is improved, but the computational overhead and training time increase
Solution Approach 1:
The patent implements a feedback mechanism where the accuracy metric is continuously monitored and fed back to control the synchronization process. When the accuracy improvement exceeds the threshold, synchronization is triggered; otherwise, it is skipped. This feedback loop ensures synchronization occurs only when beneficial, reducing unnecessary computational overhead and training time.
Solution Approach 2:
Instead of performing synchronization at every fixed interval, the patent applies partial action by selectively synchronizing only when the accuracy metric indicates improvement. This avoids excessive synchronization operations that would waste computational resources while still achieving accurate model convergence.
3Productivity
If synchronization interval is increased to reduce training time, then the computational overhead is reduced, but the accuracy of the final learning model deteriorates
Solution Approach 1:
The patent makes the synchronization interval dynamic by linking it to the accuracy metric. The interval extends when accuracy improvement is insufficient and contracts when improvement exceeds the threshold, creating an adaptive rhythm that balances training efficiency with model accuracy throughout the learning process.
Solution Approach 2:
The accuracy metric serves as a feedback signal that determines whether to synchronize or extend the interval. This feedback mechanism ensures that the system maintains accuracy by synchronizing when necessary while improving efficiency by skipping unnecessary synchronization events, achieving an optimal balance between the two objectives.
Data Source
AI summary
A system and a method for adaptively synchronizing learning of multiple learning models are disclosed. Several local learning models are executed on multiple nodes. Learning model parameters are shared by such nodes to a master node, in multiple iterations, after a predefined synchronization interval. Such learning model parameters are aggregated and central learning models are generated based on aggregated set of learning model parameters. Accuracies of the central learning models and an average accuracy of the central learning models are determined. Accuracy of an immediate central learning model i.e. the one received after determining the average accuracy, is compared with the average accuracy. Based on the difference between the accuracy of the immediate central learning model and the average accuracy, the synchronization interval is modified, and the multiple nodes are updated about this modified synchronization interval.