Imagine predicting the future, not with a crystal ball, but with AI that respects your privacy. That's the promise of federated learning for time series forecasting. Traditional methods of predicting trends, like energy consumption or market fluctuations, often require vast amounts of centralized data, raising privacy concerns. Federated learning offers a solution: it allows multiple devices, like smartphones or sensors, to collaboratively train a shared prediction model without ever exchanging their raw data. A new research paper introduces "FedTime," a federated AI model built on the powerful LLaMA architecture. FedTime breaks down data into smaller "patches" and uses clever techniques like channel independence to preserve localized insights. This means edge devices, like those in a smart grid or a network of self-driving cars, can contribute to a powerful global prediction model while keeping sensitive information secure. The results? FedTime not only outperforms existing centralized methods in accuracy, especially for long-term predictions, but it also reduces communication overhead. The research shows how FedTime excels in real-world scenarios, like forecasting energy demand on the ACN dataset. This breakthrough opens exciting possibilities. Imagine personalized medicine where patients' health data helps train diagnostic models without compromising confidentiality. Or picture cities optimizing traffic flow based on data from individual vehicles, without sacrificing individual privacy. Federated time series forecasting is a significant step towards a future where AI and privacy go hand in hand.