Federated learning for smart intrusion detection systems (IDS) is an emerging research area that combines decentralized, privacy-preserving learning with cybersecurity to detect malicious activities across distributed networks. This approach enables multiple organizations or edge devices to collaboratively train deep learning models for intrusion detection without sharing sensitive network traffic or system logs, addressing privacy, compliance, and data sovereignty concerns. Research explores integration with deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and hybrid models for effective feature extraction and temporal analysis of network data. Studies also focus on handling non-i.i.d. data, communication-efficient federated optimization, robustness against adversarial attacks and data poisoning, and privacy-preserving mechanisms including differential privacy and secure aggregation. Applications include anomaly detection, malware detection, botnet identification, and adaptive threat intelligence in IoT networks, cloud infrastructures, and enterprise systems. Recent research highlights federated learning as a promising framework for building intelligent, scalable, and privacy-aware intrusion detection systems.