Large language models (LLMs) are revolutionizing how we interact with technology, but their immense data needs present a challenge. Imagine hospitals collaborating to train a medical LLM, each possessing valuable patient data but restricted by privacy regulations. How can we unlock the potential of this collective knowledge without compromising sensitive information? Researchers are exploring a groundbreaking solution: federated learning powered by blockchain and a touch of “forgetting.” Federated learning allows organizations to train a shared LLM without directly exchanging data. Each participant trains the model locally and submits only the updates to a central aggregator. However, traditional federated learning lacks robust privacy and security guarantees. This is where blockchain comes in. By recording model updates and transactions on an immutable public ledger, blockchain introduces transparency and accountability. Furthermore, the use of private blockchains allows for sensitive computations and data sharing within controlled groups, adding another layer of security. The research goes further by incorporating “machine unlearning.” Using a technique called Low-Rank Adaptation (LoRA), organizations can selectively remove their data contributions if needed. This ensures compliance with data privacy regulations (like “the right to be forgotten”) and builds user trust. The research doesn’t stop at theory. Real-world case studies, such as university alliances sharing educational data and hospitals collaborating on medical LLMs, highlight the framework's potential. By combining the strengths of federated learning, blockchain, and machine unlearning, this research opens up exciting possibilities for secure, privacy-preserving collaboration on training the next generation of powerful LLMs. This could lead to more specialized, accurate, and ethical AI models, benefiting various fields like medicine, education, and finance. The future of AI collaboration may just lie in learning how to forget.