Data Reconstruction and Protection in Federated Learning for Fine-Tuning Large Language Models

Abstract

Federated learning can facilitate multiple parties to train a shared model on their own private data in a communication-efficient manner. It offers significant benefits for fine-tuning pre-trained large language models, as it supports distributed fine-tuning with a wider range of diverse data while preserving data privacy. However, recent research has revealed a potential privacy vulnerability in federated learning, specifically in the sharing of gradients from clients to server. This vulnerability can lead to the leakage of training data for Transformer-based large language models, thereby allowing the recovery of textual data. In this paper, we conduct a comprehensive evaluation of the effectiveness of the state-of-the-art gradient leakage attacks on textual data within the context of fine-tuning large language models. Our findings reveal that the key element for the attack's success — the target gradient — is not as readily obtainable for the adversary as previously assumed, particularly in regards to the Transformer architecture and practical federated learning settings. A technical error in their implementations has inadvertently caused the gradient to become more associated with the target data than intended. With the error fixed and when following the conventional federated learning framework, gradient leakage attacks pose minimal threats to large language models.

Topics & Concepts