Hyperparameter Advisor for Differentially Private Federated Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Differentially private federated learning processes face a tradeoff between data privacy and model accuracy due to the addition of noise, which affects the optimization of hyperparameters such as privacy budget, learning rate, and batch size, leading to suboptimal performance.
Innovation Solution
An analytical relationship between the stochastic gradient descent (SGD) loss function and hyperparameters like privacy budget, learning rate schedule, and batch size is established to determine optimal hyperparameters, allowing for a non-uniform distribution of noise across training iterations to improve model accuracy while maintaining differential privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noise is added to achieve differential privacy, then data privacy is improved, but model accuracy deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from uniform noise distribution to non-uniform noise distribution across training iterations. The noise schedule dynamically adjusts the amount of noise added at different stages of training, allowing the system to optimize between privacy protection and model accuracy by adapting noise levels to the specific needs of each training phase
Solution Approach 2:
The patent changes the parameter of noise distribution from uniform to non-uniform across training iterations. By varying the noise schedule parameter over time, the system achieves different privacy-accuracy tradeoffs at different stages of training, improving overall model accuracy while maintaining differential privacy guarantees
2Measurement precision
If hyperparameters are optimized for accuracy, then model accuracy is improved, but differential privacy protection deteriorates
Solution Approach 1:
The system dynamically adjusts the privacy budget allocation across training iterations rather than using a fixed uniform distribution. This dynamic approach allows the model to achieve higher accuracy in later training stages while maintaining privacy protection in earlier stages, resolving the tradeoff between accuracy optimization and privacy protection
Solution Approach 2:
The patent applies preliminary action by establishing privacy guarantees in the early training iterations before the model reaches its most accurate state. By allocating privacy budget strategically in the initial phases, the system builds differential privacy protection that persists throughout training while allowing accuracy to improve in subsequent iterations
3Ease of manufacture
If uniform noise distribution is used, then implementation is simple, but model accuracy is suboptimal
Solution Approach 1:
The patent introduces dynamics to the noise distribution mechanism, transitioning from static uniform distribution to dynamic non-uniform distribution. This dynamic noise scheduling approach, while slightly more complex in implementation, significantly improves model accuracy by adapting noise levels to training progress
Solution Approach 2:
The system changes the noise distribution parameter from uniform to non-uniform across training iterations. This parameter change enables the system to capture the evolving needs of the training process, improving accuracy while maintaining reasonable implementation complexity through structured noise scheduling
Data Source
AI summary
Techniques regarding determining hyperparameters for a differentially private federated learning process are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a hyperparameter advisor component that determines a hyperparameter for a model of a differentially private federated learning process based on a defined numeric relationship between a privacy budget, a learning rate schedule, and a batch size.