TransFed AI: A Scalable and Private Architecture for Next-Gen Forecasting

Federated Learning (FL) represents a transformative shift in how machine learning models are trained, allowing decentralized devices to collaboratively learn without compromising user data. In parallel, transformer-based architectures have emerged as the cornerstone of next-generation AI, excelling across natural language, vision, and multimodal tasks. This paper investigates how these two paradigms intersect, proposing a novel framework that embeds transformer models within federated learning environments, while integrating robust privacy preserving mechanisms. Our work tackles core challenges such as memory limitations, communication overhead, and privacy-utility tradeoffs. Experimental results confirm that our framework retains competitive performance relative to centralized approaches, while ensuring strong privacy guarantees, paving the way for scalable, private AI solutions in sensitive domains like healthcare, finance, and personal computing.