Imagine a world where companies could collaborate to squash software bugs faster, without ever sharing their sensitive code. This seemingly impossible scenario is becoming a reality thanks to federated learning applied to large language models (LLMs). Recent research explored this exciting frontier, training LLMs to fix bugs in a decentralized way. The study tackled a critical question: Can we improve automated program repair (APR) by letting AI models learn from diverse, private datasets? Using a private industrial dataset and a robust benchmark, researchers fine-tuned various cutting-edge code LLMs like CodeLlama and CodeQwen, demonstrating the power of federated learning. The results were impressive. Federated learning significantly boosted the bug-fixing abilities of these LLMs, sometimes even rivaling the performance of models trained on a massive, centralized dataset. Surprisingly, the research also found that code from different companies, with varying styles and complexities, didn't hinder the collaborative learning process. This is a game-changer, meaning companies can join forces to build more reliable software without jeopardizing their proprietary code. While this research shows the immense promise of federated learning for program repair, it also highlights some challenges. Personalized learning, which tailors models to individual needs, proved less effective in this context, suggesting that adapting these techniques to LLMs and code-specific tasks requires further exploration. This research opens doors to a new era of collaborative coding, where shared learning leads to better software for everyone, all while keeping sensitive data safe and sound.