Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs
Journal:
arXiv
Published Date:
Feb 7, 2025
Abstract
Federated learning (FL) is a popular paradigm for collaborative training
which avoids direct data exposure between clients. However, data privacy issues
still remain: FL-trained large language models are capable of memorizing and
completing phrases and sentences contained in training data when given with
their prefixes. Thus, it is possible for adversarial and honest-but-curious
clients to recover training data of other participants simply through targeted
prompting. In this work, we demonstrate that a popular and simple fine-tuning
strategy, low-rank adaptation (LoRA), reduces memorization during FL up to a
factor of 10. We study this effect by performing a medical question-answering
fine-tuning task and injecting multiple replicas of out-of-distribution
sensitive sequences drawn from an external clinical dataset. We observe a
reduction in memorization for a wide variety of Llama 2 and 3 models, and find
that LoRA can reduce memorization in centralized learning as well. Furthermore,
we show that LoRA can be combined with other privacy-preserving techniques such
as gradient clipping and Gaussian noising, secure aggregation, and Goldfish
loss to further improve record-level privacy while maintaining performance.
