Latent Space Compression for Privacy-Preserving Edge Model Training
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Solution Overview
Problem
Machine learning model training is computationally demanding and poses challenges with data privacy and efficiency, particularly when dealing with large datasets and edge devices, which often have limited computational power and may require sharing sensitive data.
Innovation Solution
The approach involves training machine learning models using self-supervised feature extractors across multiple devices, where data is compressed into a latent space and processed without sharing raw data, utilizing negative and positive samples to adjust model weights, and distributing trained models to edge devices for inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning model training is performed using centralized data collection, then model accuracy can be improved, but data privacy is compromised and bandwidth usage increases
Solution Approach 1:
The patent introduces an intermediary mechanism where instead of sharing raw data, devices share encoded representations or gradients through a centralized server. The server acts as a mediator that aggregates information from multiple devices without exposing the actual private data, thus maintaining privacy while still enabling model improvement through collective learning.
Solution Approach 2:
The patent extracts only the necessary information (such as gradient updates or encoded features) from the raw data and transmits this extracted information to the centralized server. This allows the model to learn from multiple data sources without transmitting or storing the actual sensitive data, thereby separating the useful information from the privacy-sensitive content.
2Measurement precision
If machine learning model training is performed using centralized data collection, then model accuracy can be improved, but network bandwidth consumption increases
Solution Approach 1:
The patent extracts only the essential training information (gradients, updates, or encoded features) from the raw data and transmits this compressed representation instead of the full dataset. This dramatically reduces the amount of data transmitted over the network while still providing sufficient information for model improvement.
Solution Approach 2:
The patent segments the training process into local computation phases (where devices process their own data privately) and centralized aggregation phases (where only small update parameters are transmitted). This segmentation allows model training to proceed with minimal bandwidth consumption while maintaining accuracy benefits from multiple data sources.
3Object-affected harmful factors
If machine learning models are trained on edge devices with limited computational power, then data privacy is preserved, but training effectiveness is reduced
Solution Approach 1:
The patent combines the computational resources of multiple edge devices through a centralized server that aggregates gradients or updates from all devices. This merging allows the model to benefit from the collective computational effort and diverse data distributions across devices, compensating for the limited individual device capabilities while maintaining data privacy.
Solution Approach 2:
The centralized server provides universal functionality by serving as a coordination hub that enables all edge devices to participate in model training despite their individual limitations. The server performs functions (aggregation, normalization, model updates) that individual edge devices cannot perform alone, thus enhancing training effectiveness without requiring any single device to have high computational power.
Data Source
AI summary
Examples of machine learning model training are described herein. In some examples, a method may include training, on an apparatus, an encoder machine learning model or a context machine learning model. In some examples, the method may include training the encoder machine learning model or the context machine learning model using negative samples in a latent space from emote devices and a ground truth.