Federated Learning Hyperparameter Prediction Model
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Solution Overview
Problem
In federated learning, hyperparameter tuning is inefficient due to significant changes in prediction results from varying hyperparameter settings, making it difficult to determine optimal distributed instance numbers and hyperparameters for desired learning performance and time.
Innovation Solution
An information processing method that performs distributed learning using arbitrary combinations of instance numbers and hyperparameters, acquiring learning performance, and generating a prediction model through supervised learning to predict learning performance for each combination, allowing for the specification of appropriate instance numbers and hyperparameters without the need for repeated experimentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hyperparameter tuning is performed through repeated experimentation in federated learning, then optimal learning performance can be achieved, but the time consumption and computational resources increase significantly
Solution Approach 1:
The system performs preliminary experiments with various hyperparameter combinations and distributed instance numbers before actual federated learning. The results are stored in a database to create a mapping relationship, allowing the prediction model to suggest optimal configurations without requiring repeated experimentation during actual learning tasks.
Solution Approach 2:
The system creates a prediction model that copies the patterns and relationships learned from preliminary experimental data. This model then predicts optimal hyperparameters and distributed instance numbers for new federated learning tasks, avoiding the need to repeat the expensive experimentation process while maintaining reliable learning performance.
2Productivity
If the number of distributed instances is increased to speed up parallel processing, then processing time is reduced, but the complexity of tuning hyperparameters and managing distributed resources increases
Solution Approach 1:
The prediction model automatically determines the optimal number of distributed instances and hyperparameter configurations based on the characteristics of the learning task and available resources. This self-service mechanism eliminates the need for manual tuning and complex configuration management, allowing the system to efficiently scale distributed processing without increasing operational complexity.
3Measurement precision
If extensive hyperparameter tuning is performed to improve prediction accuracy, then learning performance improves, but the computational resources and energy consumption increase
Solution Approach 1:
The system performs comprehensive hyperparameter tuning and accuracy evaluation in advance during the preliminary experiment phase. The results are cached in a database, so when actual federated learning tasks are executed, the prediction model can quickly suggest optimal hyperparameters without requiring additional computational resources for extensive tuning, thereby reducing energy consumption while maintaining high prediction accuracy.
Data Source
AI summary
Provided is a new mechanism enabling an appropriate distributed instance number or a hyperparameter to be specified with respect to a prescribed data set. An information processing method performed by an information processing apparatus having a storage device storing a prescribed learning model, and a processor, the method includes the steps of: causing, by the processor, other respective information processing apparatuses to perform, on one or a plurality of data sets, machine learning by using the prescribed learning model according to respective combinations in which an instance number and a hyperparameter learned in parallel are arbitrarily changed; acquiring, by the processor, learning performance, corresponding to the respective combinations, from the respective information processing apparatuses; performing, by the processor, supervised learning by using learning data including the respective combinations and the learning performance corresponding to the respective combinations; and generating, by the processor, a prediction model that predicts learning performance for each combination of an instance number and a hyperparameter by the supervised learning.