This paper proposes GPT-FedRec, a federated recommendation framework that leverages ChatGPT and a novel hybrid Retrieval Augmented Generation (RAG) mechanism to address data sparsity and heterogeneity in federated recommendation (FR). Traditional FR systems often represent users and items with discrete IDs, leading to performance degradation due to data sparsity and heterogeneity. In contrast, large language models (LLMs) have shown promise in recommendation tasks but face challenges such as low inference efficiency and potential hallucination. GPT-FedRec is a two-stage framework that first employs a hybrid retrieval mechanism to mine ID-based user patterns and text-based item features, then uses LLMs to re-rank the retrieved results. The hybrid retrieval mechanism and LLM-based re-ranking aim to extract generalized features from data and exploit pretrained knowledge within LLMs, overcoming data sparsity and heterogeneity in FR. The RAG approach also prevents LLM hallucination, improving recommendation performance for real-world users. Experimental results on diverse benchmark datasets demonstrate that GPT-FedRec outperforms state-of-the-art baseline methods. The framework is evaluated on five benchmark datasets, including Beauty, Games, Toys, Auto, and ML-100K. Results show that GPT-FedRec achieves significant improvements in recommendation performance, particularly in data sparse and heterogeneous FR settings. The framework also includes a sensitivity analysis and ablation study to validate its effectiveness. Overall, GPT-FedRec provides a privacy-aware solution for federated recommendation in data-sparse and data-heterogeneous scenarios.This paper proposes GPT-FedRec, a federated recommendation framework that leverages ChatGPT and a novel hybrid Retrieval Augmented Generation (RAG) mechanism to address data sparsity and heterogeneity in federated recommendation (FR). Traditional FR systems often represent users and items with discrete IDs, leading to performance degradation due to data sparsity and heterogeneity. In contrast, large language models (LLMs) have shown promise in recommendation tasks but face challenges such as low inference efficiency and potential hallucination. GPT-FedRec is a two-stage framework that first employs a hybrid retrieval mechanism to mine ID-based user patterns and text-based item features, then uses LLMs to re-rank the retrieved results. The hybrid retrieval mechanism and LLM-based re-ranking aim to extract generalized features from data and exploit pretrained knowledge within LLMs, overcoming data sparsity and heterogeneity in FR. The RAG approach also prevents LLM hallucination, improving recommendation performance for real-world users. Experimental results on diverse benchmark datasets demonstrate that GPT-FedRec outperforms state-of-the-art baseline methods. The framework is evaluated on five benchmark datasets, including Beauty, Games, Toys, Auto, and ML-100K. Results show that GPT-FedRec achieves significant improvements in recommendation performance, particularly in data sparse and heterogeneous FR settings. The framework also includes a sensitivity analysis and ablation study to validate its effectiveness. Overall, GPT-FedRec provides a privacy-aware solution for federated recommendation in data-sparse and data-heterogeneous scenarios.