The pervasive deployment of Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity and data generation. However, this expansive network also presents a vast attack surface, making robust intrusion detection critical. Traditional centralized Intrusion Detection Systems (IDS) face significant challenges in large-scale IoT environments, including privacy concerns, communication overhead, and the sheer volume and heterogeneity of data. This article proposes an enhanced real-time intrusion detection framework that leverages the synergistic capabilities of Federated Learning (FL) and Transfer Learning (TL). The framework allows IoT devices to collaboratively train a global intrusion detection model without sharing raw data, thereby preserving privacy, while utilizing pre-trained knowledge to enhance detection capabilities and adapt to evolving threats. We discuss the architectural components, data handling strategies, and the integration of FL and TL, highlighting how this approach can significantly improve detection accuracy, reduce latency, and maintain data privacy in dynamic and resource-constrained large-scale IoT networks.