Edge AI and Federated LLMOps for Latency-Critical IoT Systems in Smart Infrastructure

A growing number of IoT devices on smart infrastructure has increased the need for edge technology that is quick, private and can handle many data points. It is not possible for traditional AI models, due to latency and privacy issues, to meet these needs since they involve sharing and processing large amounts of data. This research introduces an integrated model that uses both Edge AI and Federated LLMOps to offer distributed intelligence within IoT-based smart infrastructure. Large language models can be trained together on edge devices using federated learning and LLMOps handles the lifecycle management, orchestration and deployment of these models across various environments. In the proposed design, workload-aware scheduling, optimized inference on the device and smart communication are used to minimize delays and energy waste. On practical day-to-day smart infrastructure projects, the approach speeds up inference, enables models to adapt and helps maintain privacy, making it well-suited to handle tasks such as traffic control, operating and safeguarding the energy grid and video monitoring in real time. This brings us closer to having AI-driven smart infrastructure that is reliable and can be expanded.