• Computer Science > Machine Learning [Submitted on 15 Mar 2025 (v1), last revised 24 Feb 2026 (this version, v3)] Title:A Survey on Federated Fine-tuning of Large Language Models View PDF HTML (experimental)Abstract:Large Language Models (LLMs) have demonstrated impressive success across various tasks. • Integrating LLMs with Federated Learning (FL), a paradigm known as FedLLM, offers a promising avenue for collaborative model adaptation while preserving data privacy. • This survey provides a systematic and comprehensive review of FedLLM. • We begin by tracing the historical development of both LLMs and FL, summarizing relevant prior research to set the context. • Subsequently, we delve into an in-depth analysis of the fundamental challenges inherent in deploying FedLLM. • Addressing these challenges often requires efficient adaptation strategies; therefore, we conduct an extensive examination of existing Parameter-Efficient Fine-tuning (PEFT) methods and explore their applicability within the FL framework.

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  • Computer Science > Machine Learning [Submitted on 15 Mar 2025 (v1), last revised 24 Feb 2026 (this version, v3)] Title:A Survey on Federated Fine-tuning of Large Language Models View PDF HTML (experimental)Abstract:Large Language Models (LLMs) have demonstrated impressive success across various tasks. Integrating LLMs with Federated Learning (FL), a paradigm known as FedLLM, offers a promising avenue for collaborative model adaptation while preserving data privacy. This survey provides a systematic and comprehensive review of FedLLM. We begin by tracing the historical development of both LLMs

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