A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations
Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for resource-constrained organizations. While cloud platforms could provide the needed resources, data privacy concerns make sharing sensitive information with third parties risky. A promising solution is split learning for LLM fine-tuning, which divides the model between clients and a server, allowing collaborative and secure training through exchanged intermediate data, thus enabling resource-constrained participants to adapt LLMs safely. % In light of this, a growing body of literature has emerged to advance this paradigm, introducing varied model me...
| Authors | Zihan Liu, Yizhen Wang, Rui Wang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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