Edge computing enables the computation and analytics capabilities to be brought closer to data sources. The available literature on AI solutions for edge computing primarily addresses just two edge layers. The upper layer can directly communicate with the cloud and comprises one or more IoT edge devices that gather sensing data from IoT devices present in the lower layer. However, industries mainly adopt a multi-layered architecture, referred to as the ISA-95 standard, to isolate and safeguard their assets. In this architecture, only the upper layer is connected to the cloud, while the lower layers of the hierarchy get to interact only with the neighbouring layers. Due to these added intermediate layers (and IoT edge devices) between the top and lower layers, existing AI solutions for typical two-layer edge architectures may not be directly applicable in this scenario. Moreover, not all industries prefer to send and store their private data in the cloud. Implementing AI solutions tailored to a hierarchical edge architecture would increase response time and maintain the same degree of security by working within the ISA-95-compliant network architecture. This paper explores a possible strategy for deploying a centralized federated learning-based AI solution in a hierarchical edge architecture and demonstrates its efficacy through a real deployment scenario.