Federated Learning Noise Standard Deviation Optimization
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Solution Overview
Problem
Conventional federated learning methods face a trade-off between reducing data leakage risk and maintaining AI model accuracy, as increasing noise to reduce leakage risk decreases model accuracy.
Innovation Solution
A learning system comprising N learning apparatuses that simultaneously learn the standard deviation of noise while updating the AI model, exchanging update differences of both model parameters and noise standard deviations to optimize noise magnitude and maintain learning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more noise is added to reduce data leakage risk, then security is improved, but model accuracy deteriorates
Solution Approach 1:
The patent applies dynamics by making the noise standard deviation adjustable and learnable rather than fixed. The system dynamically adapts the noise level during federated learning by introducing a learnable parameter σ that controls noise magnitude, allowing the system to optimize the balance between privacy protection and model accuracy based on learning progress and requirements
Solution Approach 2:
The patent changes the parameter of noise standard deviation from a fixed value to a learnable parameter σ. By treating the noise level as a configurable parameter that can be optimized through gradient descent alongside model parameters, the system can adjust noise magnitude to achieve optimal privacy-accuracy tradeoff, directly addressing the contradiction between security and accuracy
2Reliability
If noise is added to protect privacy, then security is improved, but learning performance deteriorates
Solution Approach 1:
The patent implements feedback by using gradient descent to optimize the noise standard deviation parameter σ based on its impact on model accuracy. The system continuously monitors learning performance and adjusts the noise level accordingly, creating a feedback loop that balances privacy protection with learning effectiveness, preventing excessive noise from unduly harming learning speed
3Device complexity
If fixed noise level is used to simplify the system, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent applies self-service by enabling the system to automatically determine the optimal noise level through learning. Instead of requiring manual configuration or complex external control, the system self-adjusts the noise standard deviation parameter σ through gradient-based optimization, achieving adaptability while maintaining relative system simplicity through automated self-tuning
Data Source
AI summary
A learning apparatus updates a model variable wi by using a dual variable zA and noise Rσi including a random number R in a normal distribution and a standard deviation σi of noise, obtains a parameter λ used when learning of an update difference yA and the standard deviation of noise is performed by using the updated model variable wi and the noise Rσi, exchanges the update difference yA when communication with another learning apparatus constituting the learning system is performed, updates the standard deviation σi of noise by using a dual variable zB, a hyperparameter L, and noise Rλ including a random number R in a normal distribution and the parameter 2, obtains an update difference yB by using the updated standard deviation σi, the hyperparameter L, and the noise Rλ, and exchanges the update difference yB when communication with the other learning apparatus is performed.