Distributed Machine Learning Model Training with L-BFGS
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Solution Overview
Problem
Training machine learning models on large datasets is time-consuming and requires significant data transmission, and re-training models when input data changes is inefficient.
Innovation Solution
Implementing a distributed model training system using multiple training nodes that employ the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) process, where data is processed in parallel and reduced feature vectors are used to minimize data transmission and accelerate re-training by utilizing previous model states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gradient descent training is used on large datasets, then model training accuracy can be achieved, but the training time becomes excessively long
Solution Approach 1:
The patent divides the large dataset into multiple smaller batches or subsets that can be processed in parallel across multiple computing nodes. Each node trains on a portion of the data independently, and results are aggregated to form the final model. This segmentation enables faster training while maintaining accuracy by distributing the computational load.
Solution Approach 2:
The patent performs preliminary data processing and feature extraction before the main training phase. By pre-processing the dataset to extract relevant features and reduce dimensionality beforehand, the actual training process requires fewer computational iterations, significantly reducing training time while preserving model accuracy.
2Measurement precision
If complete datasets are transmitted to server computers for training, then comprehensive model training is achieved, but the amount of data transmission increases significantly
Solution Approach 1:
The patent extracts only the essential features and statistics from the complete dataset that are necessary for model training, rather than transmitting the entire dataset. By identifying and transmitting only the critical data elements needed for training, the system achieves comprehensive model training with minimal data transmission.
Solution Approach 2:
Instead of transmitting the complete original dataset, the patent creates and transmits compressed representations or statistical summaries (such as mean, variance, and other aggregated features) that capture the essential characteristics of the data. These copies contain sufficient information for training while occupying minimal transmission bandwidth.
3Measurement precision
If model re-training is performed when input data changes, then model accuracy is maintained, but the re-training time becomes excessively long
Solution Approach 1:
The patent maintains pre-computed statistical information and feature representations from the original training data. When re-training is needed due to data changes, the system leverages these pre-computed elements as a starting point, performing only incremental updates rather than complete re-training. This dramatically reduces re-training time while maintaining model accuracy.
Solution Approach 2:
The patent employs techniques that allow the model to adapt to data changes by adjusting specific parameters or weights based on the changes detected, rather than re-training the entire model from scratch. By identifying which parameters need adjustment and updating only those, the system maintains accuracy with minimal re-training time.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes receiving a complete set of training data; receiving instructions to train a predictive model having a plurality of parameters on an initial subset of the complete set of training data; training the predictive model on the initial subset; storing data representing a first state of the predictive model after training the predictive model on the initial subset; receiving updated parameter values and instructions to train the predictive model on a new subset of the complete set of training data; and training the predictive model on the new subset.