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Integration of Large Language Models and Federated Learning

Chaochao Chen, Xiaohua Feng, Yuyuan Li, Lingjuan Lyu, Jun Zhou, Xiaolin Zheng, Jianwei Yin

TL;DR

This paper surveys the integration of Large Language Models (LLMs) with Federated Learning (FL) and proposes a three-part framework for combining LLM sub-technologies with FL, FL sub-technologies with LLMs, and the overall FedLLMs concept. It reviews current progress, identifies key advantages, challenges, and future directions, and discusses practical applications in healthcare, finance, and education. The authors analyze architectural design choices, privacy-preserving methods, data bias and copyright concerns, and incentive mechanisms essential for sustained collaboration, offering guidance for unified evaluation benchmarks. By highlighting the complementarity of LLMs and FL, the work aims to accelerate privacy-conscious, multi-party development of high-quality, domain-specific large models.

Abstract

As the parameter size of Large Language Models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data. In response, existing research has attempted to make a breakthrough by incorporating Federated Learning (FL) into LLMs. Conversely, considering the outstanding performance of LLMs in task generalization, researchers have also tried applying LLMs within FL to tackle challenges in relevant domains. The complementarity between LLMs and FL has already ignited widespread research interest. In this paper, we aim to deeply explore the integration of LLMs and FL. We propose a research framework, dividing the fusion of LLMs and FL into three parts: the combination of LLM sub-technologies with FL, the integration of FL sub-technologies with LLMs, and the overall merger of LLMs and FL. We first provide a comprehensive review of the current state of research in the domain of LLMs combined with FL, including their typical applications, integration advantages, challenges faced, and future directions for resolution. Subsequently, we discuss the practical applications of the combination of LLMs and FL in critical scenarios such as healthcare, finance, and education, and provide new perspectives and insights into future research directions for LLMs and FL.

Integration of Large Language Models and Federated Learning

TL;DR

This paper surveys the integration of Large Language Models (LLMs) with Federated Learning (FL) and proposes a three-part framework for combining LLM sub-technologies with FL, FL sub-technologies with LLMs, and the overall FedLLMs concept. It reviews current progress, identifies key advantages, challenges, and future directions, and discusses practical applications in healthcare, finance, and education. The authors analyze architectural design choices, privacy-preserving methods, data bias and copyright concerns, and incentive mechanisms essential for sustained collaboration, offering guidance for unified evaluation benchmarks. By highlighting the complementarity of LLMs and FL, the work aims to accelerate privacy-conscious, multi-party development of high-quality, domain-specific large models.

Abstract

As the parameter size of Large Language Models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data. In response, existing research has attempted to make a breakthrough by incorporating Federated Learning (FL) into LLMs. Conversely, considering the outstanding performance of LLMs in task generalization, researchers have also tried applying LLMs within FL to tackle challenges in relevant domains. The complementarity between LLMs and FL has already ignited widespread research interest. In this paper, we aim to deeply explore the integration of LLMs and FL. We propose a research framework, dividing the fusion of LLMs and FL into three parts: the combination of LLM sub-technologies with FL, the integration of FL sub-technologies with LLMs, and the overall merger of LLMs and FL. We first provide a comprehensive review of the current state of research in the domain of LLMs combined with FL, including their typical applications, integration advantages, challenges faced, and future directions for resolution. Subsequently, we discuss the practical applications of the combination of LLMs and FL in critical scenarios such as healthcare, finance, and education, and provide new perspectives and insights into future research directions for LLMs and FL.
Paper Structure (19 sections, 3 figures, 5 tables)

This paper contains 19 sections, 3 figures, 5 tables.

Table of Contents

  1. Pre-training.
  2. Prompts engineering.
  3. Bias between the pre-training data for LLMs and the data used for FL.
  4. Data bias transmission issues.
  5. Data privacy and copyright issues.
  6. Combination of black-box LLMs and FL.
  7. Distributed computing.
  8. Privacy-preserving computation.
  9. Additional communication overhead.
  10. Model property rights issues.
  11. Synchronisation problems due to differences in computing resources.
  12. Incentive mechanisms for FedLLMs.
  13. Continuous data stream.
  14. Personalisation issues in FedLLMs.
  15. Additional security and privacy threats.
  16. ...and 4 more sections

Figures (3)

  • Figure 1: The diagram illustrates the problem of data scarcity in LLMs. None of the hospitals have enough data for training LLMs and they are reluctant to share data with each other.
  • Figure 2: Overview of the analysis process combining LLMs and FL. We sequentially analyze the integration of sub-technologies within LLMs with FL, the integration of sub-technologies within FL with LLMs, and the overall framework combining LLMs and FL. This includes the current status of integration, the advantages brought by the combination, potential challenges, and future directions for solutions.
  • Figure 3: The current implementation framework of FedLLMs. The use of FedLLM primarily includes two phases: training and inference. The training phase further comprises pre-training, instruction-tuning, and alignment-tuning. Our subfigures below detail feasible methods for implementing each part.