Distributed Learning With Attenuated Noise for Privacy-Accuracy Balance
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Solution Overview
Problem
Existing distributed machine learning methods face challenges in balancing privacy protection and data analysis accuracy due to the use of differential privacy, where setting the clipping threshold for gradient clipping is difficult, and noise variance and gradient deviation impact model training.
Innovation Solution
An adaptive privacy-protection distributed learning method based on attenuated noise perturbation, which stepwise adjusts clipping thresholds and noise intensity coefficients over time intervals, using adaptive historical gradient aggregation to reduce noise errors and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If differential privacy is applied to protect user privacy in distributed machine learning, then privacy protection is improved, but data analysis accuracy deteriorates due to noise perturbation
Solution Approach 1:
The patent applies dynamics by making the noise perturbation strength adaptive rather than fixed. The noise intensity coefficient is dynamically adjusted based on the iteration number and communication topology changes, allowing the system to optimize between privacy protection and accuracy at different training stages. This resolves the contradiction by enabling strong privacy protection early in training when data is most sensitive, while reducing noise intensity later when model convergence is prioritized.
Solution Approach 2:
The patent changes the parameter of noise intensity coefficient from a static value to a dynamic one that varies with iteration number and communication conditions. By adjusting the noise perturbation strength as a variable parameter rather than a fixed constant, the system can adaptively balance privacy protection and data analysis accuracy throughout the training process, resolving the fundamental contradiction between these two objectives.
2Reliability
If gradient clipping threshold is set to limit noise variance, then privacy protection is improved, but model training accuracy deteriorates due to gradient deviation
Solution Approach 1:
The patent makes the gradient clipping threshold dynamic by adjusting it according to the iteration number and communication topology changes. The clipping threshold is not fixed but adapts over time, allowing the system to apply stronger clipping when privacy is prioritized and weaker clipping when accuracy is prioritized, thus resolving the contradiction between privacy protection and training accuracy.
Solution Approach 2:
The patent implements periodic adjustment of the clipping threshold based on communication topology changes and training progress. By periodically re-evaluating and adjusting the clipping parameter rather than maintaining a static setting, the system can synchronize privacy protection levels with training stages, resolving the contradiction between these competing objectives.
3Ease of manufacture
If fixed noise intensity is used throughout training, then implementation is simplified, but performance deteriorates due to inability to adapt to communication topology changes
Solution Approach 1:
The patent transforms the fixed noise intensity coefficient into a dynamic one that automatically adapts to communication topology changes and training progress. This dynamic adjustment mechanism, while adding some computational complexity, significantly improves training performance by optimizing noise perturbation at each stage, resolving the contradiction between implementation simplicity and training performance.
Solution Approach 2:
The patent introduces feedback mechanisms where the noise intensity coefficient and clipping threshold are adjusted based on observed communication topology changes and training performance. This feedback-driven adaptation allows the system to automatically optimize parameters without manual intervention, resolving the contradiction between simple implementation and high performance by using automated feedback loops.
Data Source
AI summary
This disclosure belongs to the technical field of distributed machine learning, and specifically relates to an adaptive privacy-protection distributed learning method and device based on attenuated noise perturbation. The method includes: acquiring a local gradient of a node according to a sample gradient after node clipping, wherein a clipping threshold of the node decreases with the increase of iteration rounds; injecting Gaussian noise into the local gradient, wherein the intensity of the Gaussian noise is stepwise attenuated with the increase of iteration rounds; aggregating the local gradient of the node after injection of the Gaussian noise in each iteration round, and using the aggregated gradient to update local model parameters, and broadcasting the updated local model parameters to adjacent nodes for parameter updating; and then aggregating the updated model parameters of the adjacent nodes for the next iteration.