Federated Edge Intelligence for Low-Latency Intrusion Detection in Smart Homes

The increasing number of smart home devices has created critical security concerns that call for sophisticated intrusion detection systems. This research develops a distributed, AI-based smart home Intrusion Detection System (IDS) which utilizes machine learning, federated learning, and edge intelligence for anomaly detection in IoT devices, all while safeguarding user privacy. At the core of the system is federated learning which permits group-based training without exposing raw data, safeguarding privacy, mitigating datasharing issues, and lowering network load. Applying public IoT intrusion datasets for evaluation showed that the IDS have a strong detection rate while incurring low processing costs. Because of its distributed nature, the system is able to span a large number of IoT devices and provide real-time responses to emerging threats. This study resolves major concerns such as device diversity, data confidentiality, delays, and system expandability, providing smart home systems with reliable and practical security that strengthens threat detection and maintains optimal system performance.