Fine-Tuning Large Language Models in Federated Learning Settings: A Comprehensive Solution

Federated learning (FL) has emerged as a solution for natural language understanding and generation tasks when entities cannot share their local data due to privacy concerns or regulations. However, fine-tuning large language models (LLMs) in FL settings poses significant challenges, including optimizing communication and computational resources, preparing data for different tasks, and protecting information. To address these challenges, researchers have developed the Federated Scope LLM (FSLLM) package, which consists of a benchmarking pipeline, federated parameter efficient finetuning algorithm implementations, and accelerating operators and resource-efficient operators. This comprehensive solution enables entities to fine-tune LLMs in FL settings with low communication and computation costs, even without accessing the full model.

Can Large Language Models Be Fine-Tuned in Federated Learning Settings?

The fine-tuning of large language models (LLMs) has become a crucial step in various natural language understanding and generation tasks. However, when multiple entities have similar interested tasks but cannot share their local data due to privacy concerns or regulations, federated learning (FL) emerges as a solution. FL not only avoids direct data sharing but also provides rigorous data privacy protection, model intellectual property protection, and model customization via composition with different techniques.

Despite the benefits of FL, fine-tuning LLMs in this setting still lacks adequate support from existing FL frameworks. This is because it requires optimizing the consumption of significant communication and computational resources, various data preparation for different tasks, and distinct information protection demands. In this paper, we discuss these challenges in detail and introduce our implemented package, Federated Scope LLM (FSLLM), as a main contribution.

FSLLM: A Comprehensive Package for Fine-Tuning LLMs in FL Settings

The FSLLM package consists of three main components:

1. Benchmarking Pipeline: We build an end-to-end benchmarking pipeline that automates the processes of dataset preprocessing, federated finetuning execution or simulation, and performance evaluation on federated LLM fine-tuning with different capability demonstration purposes.
2. Federated Parameter Efficient Finetuning (PEFT) Algorithm Implementations: We provide comprehensive and off-the-shelf implementations of PEFT algorithms that enable future extensions to enhance the capabilities of LLMs in FL scenarios with low communication and computation costs, even without accessing the full model (e.g., closed-source LLMs).
3. Accelerating Operators and Resource-Efficient Operators for Finetuning LLMs: We adopt several accelerating operators and resource-efficient operators for finetuning LLMs with limited resources and flexible pluggable subroutines for interdisciplinary studies, such as LLMs in personalized FL.

Challenges of Fine-Tuning LLMs in Federated Learning Settings

Fine-tuning LLMs in federated learning settings poses several challenges:

1. Optimizing Communication and Computational Resources: Fine-tuning LLMs requires optimizing the consumption of significant communication and computational resources, which can be a major bottleneck.
2. Data Preparation for Different Tasks: Each entity may have different tasks and data preparation requirements, making it challenging to develop a unified approach.
3. Information Protection Demands: Federated learning settings require rigorous data privacy protection, model intellectual property protection, and model customization via composition with different techniques.

Experimental Results

We conduct extensive and reproducible experiments to validate the effectiveness of our FSLLM package:

1. Benchmarking Pipeline: Our benchmarking pipeline demonstrates the feasibility of fine-tuning LLMs in federated learning settings.
2. PEFT Algorithm Implementations: The PEFT algorithm implementations show significant improvements in communication and computation efficiency.
3. Accelerating Operators and Resource-Efficient Operators: Our accelerating operators and resource-efficient operators demonstrate improved performance with limited resources.

Conclusion

Fine-tuning LLMs in federated learning settings is a challenging task that requires optimizing communication and computational resources, data preparation for different tasks, and information protection demands. Our FSLLM package provides a comprehensive solution to these challenges by introducing a benchmarking pipeline, PEFT algorithm implementations, and accelerating operators and resource-efficient operators. This package enables entities to fine-tune LLMs in FL settings with low communication and computation costs, even without accessing the full model.