FedP$^2$EFT: Federated Learning to Personalize Parameter Efficient Fine-Tuning for Multilingual LLMs
Journal:
arXiv
Published Date:
Feb 5, 2025
Abstract
Federated learning (FL) has enabled the training of multilingual large
language models (LLMs) on diverse and decentralized multilingual data,
especially on low-resource languages. To improve client-specific performance,
personalization via the use of parameter-efficient fine-tuning (PEFT) modules
such as LoRA is common. This involves a personalization strategy (PS), such as
the design of the PEFT adapter structures (e.g., in which layers to add LoRAs
and what ranks) and choice of hyperparameters (e.g., learning rates) for
fine-tuning. Instead of manual PS configuration, we propose FedP$^2$EFT, a
federated learning-to-personalize method for multilingual LLMs in cross-device
FL settings. Unlike most existing PEFT structure selection methods, which are
prone to overfitting low-data regimes, FedP$^2$EFT collaboratively learns the
optimal personalized PEFT structure for each client via Bayesian sparse rank
selection. Evaluations on both simulated and real-world multilingual FL
benchmarks demonstrate that FedP$^2$EFT largely outperforms existing
personalized fine-tuning methods, while complementing a range of existing FL
methods.
