Federated Language Model Training with Differential Privacy
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Solution Overview
Problem
Conventional systems for training language models fail to effectively protect sensitive information due to high error rates in identifying confidential information, leading to potential exposure of private data.
Innovation Solution
A privacy system trains machine learning models by sampling subsets of sensitive and non-sensitive data, using federated learning with gradient clipping and Gaussian noise to update model parameters, considering non-sensitive samples as a single class of sensitive samples to preserve privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems identify and mask confidential information in training data, then privacy protection is attempted, but high error rates in identification lead to misclassification and potential exposure of sensitive data
Solution Approach 1:
Instead of trying to identify and protect only sensitive information (which has high error rates), the patent inverts the approach by treating all client-associated data as potentially sensitive. The system applies differential privacy to all training samples from each client, ensuring that whether data is correctly or incorrectly classified as sensitive, the privacy guarantees hold. This inversion eliminates the reliability problem of identification accuracy.
Solution Approach 2:
The patent applies different privacy mechanisms to different parts of the data processing pipeline. At the client level, it uses federated learning to keep data local. At the sample level, it applies differential privacy with carefully calibrated noise to individual gradients. This local quality approach ensures that privacy protection is applied where it is most needed without requiring perfect identification of sensitive information.
2Reliability
If federated learning with differential privacy is applied to all client data, then privacy protection is improved, but model training complexity and computational overhead increase
Solution Approach 1:
The patent changes key parameters of the differential privacy implementation to balance privacy and complexity. It uses a calibrated noise scale parameter that adapts based on the privacy budget and data characteristics. By carefully tuning these parameters, the system achieves strong privacy guarantees without excessive computational overhead or system complexity.
Solution Approach 2:
The system implements automatic privacy budget management and adaptive noise calibration that requires minimal manual configuration. The differential privacy mechanism automatically adjusts its parameters based on the federated learning process state, reducing the operational complexity for users while maintaining strong privacy protection.
3Reliability
If noise is added to protect privacy during training, then confidential information protection is improved, but model performance and accuracy may deteriorate
Solution Approach 1:
The patent applies differential privacy noise selectively rather than uniformly to all training processes. It uses adaptive noise scaling that applies stronger privacy protection only when necessary based on the sensitivity of the particular update and the remaining privacy budget. This partial action approach maintains model performance while achieving the required privacy guarantees.
Solution Approach 2:
The system implements privacy budget allocation across training iterations in a periodic manner. Rather than applying maximum noise throughout training, it distributes the privacy budget over time, applying stronger noise in later iterations when the model is closer to convergence. This periodic approach balances privacy protection with model performance maintenance.
Data Source
AI summary
In implementations of systems for training language models and preserving privacy, a computing device implements a privacy system to predict a next word after a last word in a sequence of words by processing input data using a machine learning model trained on training data to predict next words after last words in sequences of words. The training data describes a corpus of text associated with clients and including sensitive samples and non-sensitive samples. The machine learning model is trained by sampling a client of the clients and using a subset of the sensitive samples associated with the client and a subset of the non-sensitive samples associated with the client to update parameters of the machine learning model. The privacy system generates an indication of the next word after the last word in the sequence of words for display in a user interface.