[Paper Review] Federated Large Language Models: Current Progress and Future Directions

Yuhang Yao, Jianyi Zhang+12 more|arXiv (Cornell University)|Sep 24, 2024

Topic Modeling4 citations

TL;DR

This survey provides a comprehensive overview of federated large language models (FedLLMs), focusing on recent advances in federated fine-tuning and prompt learning. It identifies key challenges such as data and model heterogeneity, communication efficiency, and privacy, while proposing future research directions including federated pre-training, on-device inference, and LLMs for synthetic data generation in FL.

ABSTRACT

Large language models are rapidly gaining popularity and have been widely adopted in real-world applications. While the quality of training data is essential, privacy concerns arise during data collection. Federated learning offers a solution by allowing multiple clients to collaboratively train LLMs without sharing local data. However, FL introduces new challenges, such as model convergence issues due to heterogeneous data and high communication costs. A comprehensive study is required to address these challenges and guide future research. This paper surveys Federated learning for LLMs (FedLLM), highlighting recent advances and future directions. We focus on two key aspects: fine-tuning and prompt learning in a federated setting, discussing existing work and associated research challenges. We finally propose potential directions for federated LLMs, including pre-training, federated agents, and LLMs for federated learning.

Motivation & Objective

• To address the growing need for privacy-preserving LLM training in data-sensitive domains such as healthcare and finance.
• To identify and analyze key challenges in federated fine-tuning and prompt learning, including data/model heterogeneity, communication costs, and security threats.
• To provide a systematic review of recent methodologies in FedLLM, focusing on parameter efficiency, convergence stability, and framework design.
• To explore emerging research directions such as federated pre-training, on-device inference, and the use of LLMs to generate synthetic FL data.
• To guide future research by highlighting open problems in scalability, ethical deployment, and integration of LLMs into federated learning pipelines.

Proposed method

• Systematically surveys recent works in federated fine-tuning and prompt learning, categorizing approaches by their handling of data/model heterogeneity, privacy, and efficiency.
• Classifies methods based on their use of parameter-efficient fine-tuning (e.g., LoRA, adapters) and communication optimization techniques (e.g., quantization, sparsification).
• Reviews frameworks for cross-silo and cross-device FL, including FedRDMA, OpenFedLLM, and FederatedScope-LLM, to assess scalability and real-world applicability.
• Analyzes evaluation benchmarks such as FedLLM-Bench and Profit, and convergence analysis tools like FedPEAT and FedMeZO to assess model stability.
• Proposes novel directions, including federated pre-training with model compression and fully-sharded data parallelism, and explores LLMs as synthetic data generators for FL.
• Examines the use of LLMs to enhance FL through knowledge distillation, prompt engineering, and capacity augmentation to improve convergence and performance on limited data.

Experimental results

Research questions

• RQ1 How can federated fine-tuning of LLMs be made efficient and robust under data and model heterogeneity?
• RQ2 What techniques enable privacy-preserving, secure, and communication-efficient training of LLMs in federated settings?
• RQ3 How can parameter-efficient methods like LoRA and adapters be adapted to maintain performance while reducing computational cost in FedLLM?
• RQ4 What are the key challenges and opportunities in federated pre-training of LLMs, especially with limited hardware and data?
• RQ5 How can LLMs be leveraged to generate synthetic data or improve FL performance through knowledge distillation and prompt engineering?

Key findings

• Data heterogeneity in FedLLM is mitigated through knowledge distillation and adapter-based regularization, as demonstrated by FedDAT and RaFFM.
• Communication efficiency is significantly improved via model compression, sparsification, and quantization, with methods like CEFHRI and FedKSeed reducing bandwidth usage.
• Parameter-efficient fine-tuning techniques such as LoRA and SLoRA enable high performance with minimal compute overhead, enabling deployment on resource-constrained devices.
• Federated pre-training is becoming feasible through fully-sharded data parallelism and model compression, reducing memory and communication costs.
• LLMs can generate high-quality synthetic data that enhances generalization and mitigates data scarcity, though careful management is needed to avoid privacy leaks.
• Ethical and legal risks in synthetic data generation require robust frameworks to prevent data leakage and IP violations, especially in regulated domains.