Editing Federated Learning (section)

=== 5.7.5 Industrial IoT ===

Industrial IoT (IIoT) environments—such as smart manufacturing plants, oil refineries, power grids, and logistics hubs—generate massive volumes of operational data from distributed sensors, controllers, and machines. Federated Learning (FL) enables these IIoT systems to collaboratively train models across different factories, production lines, or machine clusters without transmitting raw industrial data to centralized servers. This is particularly important for preserving proprietary process information, complying with regulatory frameworks, and ensuring low-latency processing at the edge<sup>[1][3]</sup>.

One core use case is *predictive maintenance*, where FL allows edge devices embedded in machines (e.g., motors, turbines, robots) to learn failure patterns from local sensor readings like vibration, temperature, or acoustic signals. These localized models are periodically aggregated to improve a shared global model capable of predicting failures across different machinery types or operational settings. This improves equipment uptime and safety while avoiding central storage of confidential operational data<sup>[2][4]</sup>.

FL is also used in *quality control and defect detection*. Visual inspection systems in different factories can collaborate by training computer vision models that detect defects in manufacturing processes. FL allows knowledge sharing across factories that may use similar processes or materials, while respecting the industrial secrecy of each plant. By preserving data locality, FL helps manufacturers share model improvements without risking intellectual property exposure<sup>[3]</sup>.

In supply chain monitoring, FL enables logistics nodes, warehouses, and distribution centers to coordinate inventory forecasting and optimize routing models based on local data. These systems benefit from FL’s ability to respect competitive boundaries among vendors while improving the efficiency of global supply chains<sup>[4][5]</sup>.

Challenges in IIoT-based FL include highly non-IID data due to variation in production processes, limited compute resources on industrial edge devices, and communication constraints in isolated environments. To mitigate these, IIoT deployments often leverage hierarchical FL (e.g., plant-level edge servers aggregating from device clusters) and lightweight model architectures designed for embedded processors<sup>[1][2]</sup>. As industrial systems become increasingly connected, FL is emerging as a key enabler of secure, decentralized, and intelligent automation across critical infrastructure and smart manufacturing.