FedP$^2$EFT: Federated Learning to Personalize PEFT for Multilingual LLMs

The recent publication of 'FedP$^2$EFT' marks a significant advancement in the field of multilingual large language models (LLMs) through the introduction of a federated learning method designed to personalize parameter-efficient fine-tuning (PEFT). This method addresses the common pitfalls of existing PEFT strategies, particularly their tendency to overfit in low-data scenarios. By employing Bayesian sparse rank selection, FedP$^2$EFT enables the collaborative learning of optimal PEFT structures tailored to individual clients. Evaluations on both simulated and real-world multilingual federated learning benchmarks have shown that this approach significantly outperforms traditional personalized fine-tuning methods. The implications of this research are profound, as it enhances the adaptability of multilingual models, making them more effective in diverse linguistic contexts and ultimately supporting better communication and understanding across languages.