{"ID":2879390,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16261","arxiv_id":"2508.16261","title":"On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility View","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.","short_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 acc...","url_abs":"https://arxiv.org/abs/2508.16261","url_pdf":"https://arxiv.org/pdf/2508.16261v1","authors":"[\"Tao Guo\",\"Junxiao Wang\",\"Fushuo Huo\",\"Laizhong Cui\",\"Song Guo\",\"Jie Gui\",\"Dacheng Tao\"]","published":"2025-08-22T09:52:31Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}