Federated Learning (FL) enables training models across decentralized data silos while preserving client data privacy. Recent research has explored efficient methods for post-training large language models (LLMs) within FL to address computational and communication challenges. While existing approaches often rely on access to LLMs' internal information, which is frequently restricted in real-world scenarios, an inference-only paradigm (black-box FedLLM) has emerged to address these limitations. This paper presents a comprehensive survey on federated tuning for LLMs. We propose a taxonomy categorizing existing studies along two axes: model access-based and parameter efficiency-based optimization. We classify FedLLM approaches into white-box, gray-box, and black-box techniques, highlighting representative methods within each category. We review emerging research treating LLMs as black-box inference APIs and discuss promising directions and open challenges for future research.
@article{arxiv.2508.16261,
title = {On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility View},
author = {Tao Guo and Junxiao Wang and Fushuo Huo and Laizhong Cui and Song Guo and Jie Gui and Dacheng Tao},
journal= {arXiv preprint arXiv:2508.16261},
year = {2025}
}