Imagine building a complex structure with LEGOs, brick by brick. Now, imagine building an advanced AI model in the same way. That's the core idea behind LEGO, a groundbreaking new technique for creating powerful language models (LLMs). Traditional LLMs, like those powering chatbots and virtual assistants, are resource-intensive, demanding vast amounts of data and processing power. This makes them difficult to deploy on smaller devices and raises privacy concerns when handling sensitive user data. LEGO offers a clever solution by building smaller, specialized language models (SLMs) from a larger LLM, like breaking down a large LEGO castle into individual towers and walls. These SLMs can be fine-tuned on individual devices with user-specific data, preserving privacy. Then, like assembling LEGO blocks, these smaller, trained models are combined to reconstruct a much larger, more robust LLM. This innovative process leverages a distributed learning system called Federated Learning, allowing individual devices to contribute to the overall model's intelligence without sharing their private data. The results are impressive. LEGO not only allows for faster training and operation on devices with limited resources but also creates models that are just as accurate, if not more so, than traditional LLMs. Experiments show LEGO models adapting better to diverse data sets and even transferring knowledge between models of different sizes, something like combining a LEGO car with a LEGO airplane to build something entirely new. While LEGO demonstrates remarkable potential for building more efficient and private AI systems, there are still challenges to overcome. The current method introduces some noise during the recombination process, and further research is needed to refine the assembly of these AI building blocks. Nonetheless, LEGO represents a significant step toward democratizing access to powerful AI and paving the way for more personalized, private, and efficient applications in the future.