On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility View

• URL: http://arxiv.org/abs/2508.16261v1
• Date: Fri, 22 Aug 2025 09:52:31 GMT
• Title: On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility View
• Authors: Tao Guo, Junxiao Wang, Fushuo Huo, Laizhong Cui, Song Guo, Jie Gui, Dacheng Tao,
• Abstract summary: Federated Learning (FL) enables training models across decentralized data silos while preserving client data privacy.<n>Recent research has explored efficient methods for post-training large language models (LLMs) within FL to address computational and communication challenges.<n>An inference-only paradigm (black-box FedLLM) has emerged to address these limitations.
• Score: 82.19096285469115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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