PaperlayerView exact revision on arXiv

Source-linked AI summary

Integration of Large Language Models and Federated Learning

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

arXiv:2307.08925v3cs.LGcs.AIcs.CL

TL;DR

Growing LLMs require more high-quality training data, while private data cannot be directly shared as plain text. This paper organizes research on LLM–FL integration into three strands and surveys its applications, benefits, challenges, and future directions. It reports complementarity between the technologies and discusses FedLLMs as a route toward more equitable educational models.

  • Problem

    Growing LLMs require more training data, but public data growth is slow and privacy regulations prevent direct plaintext sharing of private data.

    Source: §1 · p. 2

  • Method

    The paper organizes LLM–FL integration into LLM sub-technologies with FL, FL sub-technologies with LLMs, and the overall FedLLM framework.

    Source: §1 · p. 2

  • Results

    The review reports that studies demonstrate complementarity between LLMs and FL, with each addressing challenges in the other’s domain.

    Source: §5 · p. 15

  • Takeaways & Limitations

    FedLLMs offer a route to educational LLMs that balance the needs of different languages and cultural backgrounds.

    Source: §4 · p. 15

    Large LLM parameter counts can make communication time substantially exceed training time, with network instability potentially worsening the issue.

    Source: §3.2.2 · p. 8

Abstract

from arXiv · show

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.

1. Introduction

The data demands of growing LLMs exceed the slow growth of public data, while privacy and competition limit access to private datasets. The paper organizes prior work on LLM–FL integration into three strands and reviews its benefits, challenges, applications, and future directions.

1. Introduction

  • Growing LLMs need more training data, but public data growth is slow and privacy rules prevent direct plaintext sharing of private data.The introduction illustrates the problem with hospitals whose separate datasets may be insufficient, while a joint dataset could provide a substantial corpus.Source: §1 · p. 2
  • FL can support collaborative model development while keeping data private, and LLMs’ task generalization may help address challenges within FL.The paper presents the two technologies as complementary.Source: §1 · p. 2
  • The review addresses a gap in prior coverage by organizing LLM–FL integration across sub-technologies in LLMs, sub-technologies in FL, and the overall FedLLM concept.It also considers shared sub-technologies from multimodal and visual foundation models that may be adapted to language models.Source: §1 · p. 2
  • The paper reviews integration research and applications, including scenarios in healthcare, finance, and education.It also presents future directions for the field.Source: §1 · pp. 2–3

2. Background

The background introduces LLM capabilities, architectures, and training stages, then describes FL’s privacy-preserving collaborative learning and aggregation.

2.1 Large Language Models

  • LLMs predict future-token probabilities from token sequences and show capabilities in in-context learning, instruction following, and step-by-step reasoning.The background attributes their success to both model size and training data and the Transformer architecture.Source: §2.1 · p. 3
  • LLMs use only-decoder or encoder-decoder architectures, with only-decoder models including causal and prefix decoders.Causal decoders use unidirectional attention so each token attends only to earlier tokens and itself.Source: §2.1 · p. 3
  • LLM training proceeds through pre-training, instruction-tuning, and alignment-tuning, which respectively build language abilities and knowledge, improve task handling, and align models with human values.Instruction-fine-tuned models may generate harmful content when misaligned with human values.Source: §2.1 · p. 3

2.2 Federated Learning

  • FL lets participants collaboratively learn while keeping raw data local and asynchronously sharing updates such as weights and gradients.FedAvg aggregates participating clients’ model updates by averaging; FL research also addresses heterogeneous user data.Source: §2.2 · p. 3

3.1 Integration of Sub-technologies in LLMs with FL

The section reviews how LLM pre-training and prompt techniques are used in FL, then surveys challenges in aligning data, protecting privacy and copyright, and integrating black-box models.

3.1.1 Current Status

  • Pre-trained initialization can shorten FL convergence and mitigate data and system heterogeneity, with more stable aggregation and a smaller accuracy gap versus centralized learning under non-iid data.Studies report reduced time to reach a target error; pre-trained initialization can also reduce the accuracy gap, especially with non-iid clients [27].Source: §3.1.1 · p. 5
  • FedYolo uses communication-efficient modules to let clients handle multiple unrelated tasks with one pre-trained model while reducing catastrophic forgetting versus full updates [206].The section notes that FL typically focuses on a single task, and that mobile and edge settings need more attention.Source: §3.1.1 · p. 5
  • Prompt-based FL work personalizes models using prompts that reflect local data distributions and supports domain generalization with shared global and domain-specific prompts [91] [12].DiPrompT avoids requiring a one-to-one mapping between source domains and local clients [12].Source: §3.1.1 · pp. 5–6

3.1.2 Challenges and Future Directions

  • Mismatch between LLM pre-training data and FL client data can weaken transfer, while misaligned LLM-generated data may introduce bias and noise into FL.The section proposes improving synthetic-data quality and diversity and aligning distributions before incorporation.Source: §3.1.2 · p. 6
  • Toxicity, bias, and misinformation in synthetic data can transfer to the final FL model, motivating fairness-guided augmentation and bias-removal approaches.The section also proposes more robust FL aggregation to prevent bias introduction.Source: §3.1.2 · p. 6
  • LLM-generated FL data raises privacy and copyright risks because training-data origins can be difficult to trace and generated text may resemble private or copyrighted material [24] [22] [92].Proposed directions include checking compliance, selectively generating distinct data, and improving interpretability to identify problematic content.Source: §3.1.2 · p. 6
  • For black-box LLMs available only through APIs, knowledge distillation can use teacher outputs as pseudo-labels to train FL student models [145].This offers a route when model internals are inaccessible.Source: §3.1.2 · p. 6

3.2 Integration of Sub-technologies in FL with LLMs

This section surveys how FL technologies are applied to LLM needs, including resource sharing and prompt-related methods. It also discusses communication overhead and model ownership challenges in federated LLM training.

3.2.1 Current Status

  • FL integration combines distributed resources and data to ease individual training and inference burdens and target generalization beyond centralized datasets.Examples include LLaMA training with 2048 NVIDIA A100 GPUs for 21 days and FLM with 192 NVIDIA A800 GPUs for 22 days.Source: §3.2.1 · p. 7
  • FL privacy-preserving features support domain-specific prompt generation, addressing public-data limits that can produce generic templates and repetitive responses.The resulting prompts are intended to adapt to particular needs.Source: §3.2.1 · p. 7
  • FedLogic treats chain-of-thought prompt selection as fuzzy-score-based rule selection, with LLMs serving as rule generators.The approach targets explainability while balancing universality and personalization across domains.Source: §3.2.1 · p. 7

3.2.2 Challenges and Future Directions

  • FL communication time can exceed training time, and unstable networks may worsen overhead; training acceleration strategies do not fully resolve communication challenges.The section points to pruning, compression, and parameter-efficient fine-tuning as approaches to reduce model burdens.Source: §3.2.2 · p. 8
  • Federated LLM training raises ownership risks, including model leaks and intellectual-property infringement; watermarking is proposed to protect ownership without compromising performance.The text calls for frameworks to identify misappropriation and illegal ownership claims.Source: §3.2.2 · p. 8

3.3 Overall Integration of LLMs and FL

The section presents FedLLMs as a comprehensive approach to combining LLMs and FL, then reviews their implementation, applications, challenges, and possible directions for addressing them.

3.3.1 Current Status

  • FedLLMs aim to preserve privacy while combining high-quality data across parties, supporting task adaptation and large-model training in specific areas, and facilitating lifelong learning.The paper frames this as a broader approach than combining individual LLM and FL sub-technologies.Source: §3.3.1 · p. 8
  • FATE-LLM studies communication-efficient fine-tuning for classification, FederatedScope-LLMs and Shepherd extend to federated instruction-tuning, and OpenFedLLM adds federated alignment-tuning and empirical comparisons.The earlier instruction-tuning frameworks lack diverse training datasets; OpenFedLLM compares FL aggregation methods across datasets.Source: §3.3.1 · pp. 8–9
  • The proposed FedLLMs architecture separates training into pre-training, instruction-tuning, and alignment-tuning, followed by inference.At inference, clients can combine local and global model outputs to enhance accuracy and adaptability.Source: §3.3.1 · p. 9, Figure 3 · p. 9

3.3.2 Challenges and Future Directions

  • Resource differences and limited bandwidth can make FedLLM communication costly; hierarchical aggregation groups clients under stronger leaders before exchanging information with the server or other clusters.Matching all clients to weaker participants’ capabilities can waste the computational resources of stronger clients.Source: §3.3.2 · p. 9
  • FedLLMs must address changing client data and participation, unified-model personalization needs, and incentives suited to participants’ differing data and computational contributions.The paper points to loss-based continual-learning approaches, personalization methods, and reinforcement learning for tailored incentives.Source: §3.3.2 · pp. 9–10
  • The paper groups FedLLM security threats as utility-focused or privacy-focused, with poisoning—including textual and backdoor attacks—among the risks to model effectiveness.It notes that textual poisoning can use tags or other discrete-data perturbations, and that backdoors can arise during instruction fine-tuning.Source: §3.3.2 · pp. 10–11
  • Privacy risks include membership and attribute inference as well as model inversion; leaked gradients can enable reconstruction of embeddings.The paper focuses on these inference threats rather than costly model-extraction attacks.Source: §3.3.2 · p. 11
  • Existing defenses may not transfer seamlessly: update analysis risks privacy leakage, robust aggregation must remain compatible with LLM architectures, and adversarial training faces text-generation and resource costs.The paper also discusses pruning, distillation, and differential privacy; gradient-based DP can add computational and storage overhead and require more noise as models scale.Source: §3.3.2 · pp. 11–13

4. Discussion of Application for Combining LLMs and FL

The paper surveys practical FedLLM applications across healthcare, finance, and education, where combining LLMs and FL may address real-world problems and improve services. It also examines the distinct challenges in each setting.

Healthcare

  • FedLLMs can let healthcare institutions aggregate data to train healthcare-specific large models while addressing privacy concerns around patient information.Healthcare applications remain constrained by the characteristics of healthcare data and strict usage regulations.Source: §4 · pp. 13–14
  • Healthcare data heterogeneity, missing or imbalanced records, and the need to interpret predictions for doctors and patients constrain FedLLM implementation.These challenges arise alongside data-use regulations and can affect model generalization and accuracy.Source: §4 · p. 14

Finance

  • FedLLMs offer a path to financial models when organizations lack enough high-quality data, and their collaborative mechanism can support stricter alignment among participants.Financial applications include reasoning, claims detection, named entity recognition, and sentiment analysis.Source: §4 · p. 14
  • Financial FedLLMs must support rapid updates for dynamic markets, high inference accuracy and reliability, and contextual understanding of multi-step queries.Source: §4 · p. 14

Education

  • In education, broader participation and richer training data may reduce LLM bias and expand multilingual adaptability, supporting more equitable and inclusive models.Source: §4 · p. 15
  • Educational FedLLMs must adapt to learners’ differing stages and backgrounds, address academic and non-academic goals, guide students through reasoning, and align outputs to refuse unreasonable requests.Source: §4 · p. 15

5. Conclusion and Future Work

This paper organizes research on LLM–FL integration and reviews its advantages, challenges, future directions, and applications. It concludes that the field needs unified evaluation benchmarks and datasets.

  • Combining LLMs and FL is complementary: FL can pool data for training while preserving privacy, and LLM task generalization can help address challenges in FL.Source: §5 · p. 15
  • The paper proposes a framework for organizing ongoing integration research and analyzes its advantages, challenges, future directions, and healthcare, finance, and education applications.Source: §5 · p. 15
  • The review calls for unified evaluation benchmarks and datasets in future research on LLM–FL integration.Source: §5 · p. 15
Loading 2307.08925v3…