Imagine a world where hospitals can collaborate to improve their AI models without ever sharing sensitive patient data directly. That’s the promise of federated learning. But what if the AI models involved are massive, complex large language models (LLMs) like Gemini or Llama 2? Traditional federated learning techniques struggle with the sheer size and complexity of these LLMs. Enter LanFL, a novel approach that uses the power of prompts and synthetic data to make federated learning with LLMs a reality. LanFL lets participants train collaboratively without directly accessing the LLM's inner workings. Instead, they use prompts – like giving instructions to the LLM – and swap synthetically generated data samples. This clever trick allows the LLMs to learn from each other indirectly, respecting the privacy of the original data. This innovation has significant implications for industries handling sensitive information. Imagine banks collaborating to detect fraud more effectively, or researchers pooling data to improve disease diagnosis without compromising patient confidentiality. LanFL has been tested across several prominent LLMs and datasets, demonstrating its robustness and effectiveness. It even outperformed traditional in-context learning methods, proving the power of this collaborative approach. While promising, challenges remain. Future work could explore more advanced prompt optimization techniques and investigate the trade-offs between privacy and performance. Nevertheless, LanFL offers a glimpse into a future where powerful LLMs can be harnessed collaboratively and privately, unlocking new possibilities in various fields.