Cascaded Differential Privacy for Collaborative Learning
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Solution Overview
Problem
Collaborative learning approaches face challenges in balancing data privacy and model performance, as applying differential privacy to local parameters for securing data can degrade model accuracy and increase computation costs, while malicious entities can reverse-engineer local data from shared parameters.
Innovation Solution
A multi-stage collaborative learning approach is implemented, where differential privacy is applied in initial stages with decreasing magnitudes, followed by a final stage without privacy noise, to achieve optimal model performance while maintaining privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If differential privacy is applied to local parameters with high magnitude, then data privacy is improved, but model accuracy deteriorates
Solution Approach 1:
The patent applies dynamic differential privacy by adjusting the privacy budget allocation across training stages. In early stages, higher privacy budgets are applied to protect against model inversion attacks, while in later stages, privacy budgets are reduced to minimize accuracy degradation. This dynamic adjustment resolves the contradiction by optimizing the privacy-accuracy tradeoff at different points in the training process.
Solution Approach 2:
The training process is segmented into multiple stages with different privacy protection levels. Early training stages use stronger differential privacy to prevent attacks on initial model states, while later stages use weaker privacy protection. This segmentation allows the system to achieve adequate privacy protection without sacrificing overall model accuracy.
2Reliability
If differential privacy is applied to local parameters, then data privacy is improved, but computation costs increase
Solution Approach 1:
The patent applies differential privacy selectively rather than uniformly across all training stages and all parameters. By applying privacy protection only where necessary (early stages and critical parameters), the system achieves adequate privacy protection while reducing unnecessary computational overhead from applying full privacy protection throughout the entire training process.
3Measurement precision
If local parameters are shared for collaborative learning, then model performance is improved, but security deteriorates due to reverse-engineering risks
Solution Approach 1:
The patent applies differential privacy to local parameters before sharing them during collaborative learning. This preliminary protective action prevents malicious entities from reverse-engineering the original data from shared parameters, while still allowing the parameters to be useful for training the global model. The privacy protection is applied in advance to counteract potential security threats.
Data Source
AI summary
Systems and methods are provided for cascaded privacy decentralized learning. Examples herein provide network nodes that train local instance of a machine learning (ML) algorithm with local data over a plurality of training stages. Each network node determines local parameters at one or more iterations of training during each training stage and applies, during each training stage, an amount of differential privacy to respective local parameters. The amount of differential privacy applied during one training stage is less than an amount differential privacy applied during a preceding training stage. A leader node merges the local parameters from the network nodes and shares the merged parameters with the network nodes to provide common ML model.