Federated Learning Gradient Perturbation for Privacy-Accuracy Balance
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Solution Overview
Problem
Federated learning frameworks face challenges in protecting user privacy due to potential information exposure and privacy budget explosion, especially in high-dimensional deep learning models, where model weights or gradients may leak sensitive user data.
Innovation Solution
Implementing local differential privacy (LDP) mechanisms with gradient and data perturbation models that randomly change gradients or data to distinct values, combined with splitting and shuffling to ensure anonymity, preventing remote servers from linking client data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If local differential privacy mechanisms are implemented to protect user data, then privacy protection is improved, but model training accuracy may deteriorate due to added noise
Solution Approach 1:
The patent applies different privacy perturbation strategies to different parts of the training process. Specifically, it uses gradient perturbation during backpropagation and data perturbation during forward propagation, allowing localized privacy protection where most beneficial while preserving model accuracy in other areas.
Solution Approach 2:
The system dynamically adjusts privacy parameters (such as perturbation strength and noise levels) based on the training stage, data sensitivity, and desired accuracy-privacy tradeoff. This allows optimization of both privacy protection and model accuracy by changing parameters adaptively rather than using fixed values throughout training.
2Reliability
If more privacy perturbation is applied to protect client data, then privacy protection is improved, but communication overhead and computational complexity increase
Solution Approach 1:
The patent divides the privacy protection mechanism into separate modules: gradient perturbation handling, data perturbation handling, and aggregation protocols. Each module operates independently with optimized complexity, reducing overall computational burden compared to a monolithic privacy protection approach.
Solution Approach 2:
The system applies privacy perturbation selectively rather than uniformly to all data and operations. It focuses computational effort on protecting the most sensitive parts of the training process while using lighter perturbation schemes for less sensitive operations, optimizing the balance between privacy and complexity.
3Reliability
If gradient and data perturbation are used to prevent information leakage, then privacy protection is improved, but the distinctness from original values increases causing potential loss of useful information
Solution Approach 1:
The patent employs feedback mechanisms where the perturbed gradients and data are evaluated for their utility in model training. The perturbation parameters are adjusted based on feedback from training performance metrics, ensuring that privacy protection does not excessively degrade the quality of useful information needed for effective model learning.
Solution Approach 2:
The system converts the potentially harmful effect of perturbation (loss of information fidelity) into a benefit by using the perturbed data to train models that are more robust to noise and variations. This approach transforms information loss into improved model generalization and resilience.
Data Source
AI summary
In one embodiment, a method includes accessing a plurality of initial gradients associated with a machine-learning model from a data store associated with a first electronic device, selecting one or more of the plurality of initial gradients for perturbation, generating one or more perturbed gradients for the one or more selected initial gradients based on a gradient-perturbation model, respectively, wherein for each selected initial gradient: an input to the gradient-perturbation model comprises the selected initial gradient having a value x, the gradient-perturbation model changes x into a first continuous value with a first probability or a second continuous value with a second probability, and the first and second probabilities are determined based on x, and sending the one or more perturbed gradients from the first electronic device to a second electronic device.