Personalized ML Model Delta Compression for Scalable Storage
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Solution Overview
Problem
The limited storage space available for personalized machine learning models, particularly when fine-tuned for a large number of users, leads to a storage bottleneck, as each model consumes significant space, and existing methods like Low-Rank Adaptation (LoRA) are sub-optimal in quality.
Innovation Solution
Fine-tuning a base machine learning model to generate personalized models, calculating differences between layers to create difference models, compressing these models using singular value decomposition (SVD) to low-rank matrices, and storing these compressed models to minimize storage requirements without compromising performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full finetuning of the base machine learning model is performed for each user, then model performance quality is improved, but storage space consumption increases significantly
Solution Approach 1:
The patent segments the machine learning model into a base model and multiple difference models, where each difference model contains only the unique variations for specific users or user groups. This segmentation allows storing the large base model once and only the small difference models for each user, dramatically reducing total storage requirements while maintaining full model performance when needed.
Solution Approach 2:
Instead of storing complete copies of finetuned models for each user, the patent creates difference models that represent only the deviations from the base model. These difference models act as compact copies that can be combined with the base model to reconstruct full personalized models on-demand, reducing storage space while preserving model quality.
2Quantity of substance
If LoRA (Low-Rank Adaptation) is used to reduce storage space, then storage requirements are decreased, but model performance quality becomes sub-optimal
Solution Approach 1:
The patent changes the parameter representation by storing difference models with selectively updated parameters rather than full model parameters. The difference models contain only the necessary parameter changes for personalization, allowing flexible control over storage quality trade-offs by adjusting which parameters are differentiated and at what granularity (individual user vs. user group level).
3Quantity of substance
If difference models are compressed using SVD to low-rank matrices, then storage space is reduced by approximately 75%, but model recovery complexity increases
Solution Approach 1:
The patent replaces the mechanical storage of full model parameters with a mathematical transformation system using Singular Value Decomposition (SVD). The difference models are transformed into low-rank matrix representations that compress storage by approximately 75%. The increased recovery complexity is offset by the efficiency gains in storage and retrieval scalability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces storage needs by approximately 75% while maintaining model performance, allowing for scalable storage and retrieval of personalized models.
Implementation Method 1
compressing these models using singular value decomposition (SVD) to low-rank matrices
Data Source
AI summary
The present disclosure describes techniques for implementing scalable storage of personalized machine learning models. A plurality of personalized machine learning models are generated based on finetuning a base machine learning model. The base machine learning model comprises a first set of layers. Each of the plurality of personalized machine learning models comprises a second set of layers. A plurality of difference models are generated by computing differences between the first set of layers and the second set of layers. The plurality of difference models corresponds to the plurality of personalized machine learning models, respectively. The plurality of difference models are processed by compressing parameters of each of the plurality of difference models to generate a plurality of compressed models. The plurality of compressed models are stored for future use of the plurality of personalized machine learning models.