This survey offers a structured overview of the federated LLM ecosystem. We present a comprehensive taxonomy encompassing system architectures, advanced data strategies for addressing heterogeneity, and retrieval-augmented generation in federated contexts. Additionally, we review efficient adaptation methods that enable LLM tuning on resource-constrained clients and analyze data security and privacy concerns. We conclude by summarizing emerging applications in healthcare, industry, software engineering, and finance, and by outlining open problems and research opportunities for scalable, secure, and responsible federated LLM deployment.
The cover image was created with AI-generated content via GPT-Image-2, and it contains no copyrighted elements or misleading representations.
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