Unlocking Privacy-Preserving Model Training: Exploring Tokenizers with Private Federated Learning

Private Federated Learning: Training a Tokenizer for Free

Private federated learning (PFL) enables training models on private data without compromising privacy. PFL is especially efficient for fixed-parameter models like neural networks. However, this work focuses on training tokenizers, which are not fixed-parameter models. Let’s dive into the details!

Tokenizers play a crucial role in natural language processing (NLP) tasks. They are responsible for splitting text into smaller units called tokens. These tokens are then used for various NLP applications such as sentiment analysis, machine translation, and text generation.

What is Private Federated Learning?

Private federated learning, also known as federated learning with differential privacy, allows models to be trained on decentralized data across multiple devices. This decentralized approach ensures that individual user data remains private.

The Significance of PFL for Tokenizers

PFL is highly beneficial for models with a fixed number of parameters, like neural networks. However, tokenizers do not fall under this category as they have a variable number of parameters. Therefore, the challenge lies in applying PFL to train tokenizers effectively.

Training tokenizers entails the division of private data among users and leveraging their collective knowledge to improve the tokenizer model. By using PFL, privacy is preserved as the user data remains decentralized, and only aggregated model updates are shared.

Moreover, this research introduces a method to train tokenizers using PFL, enabling accurate tokenization while maintaining privacy. The proposed approach ensures efficient collaboration and contributes to the progress of NLP applications.

If you’re interested in the technical details and want to explore further, you can access the full paper here.

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