Resource-Efficient Clustered Federated Learning Framework for Industry 4.0 Edge Devices

Industry 4.0 is an aggregate of recent technologies including artificial intelligence, big data, edge computing, and the Internet of Things (IoT) to enhance efficiency and real-time decision-making. Industry 4.0 data analytics demands a privacy-focused approach, and federated learning offers a viable solution for such scenarios. It allows each edge device to train the model locally using its own collected data and shares only the model updates with the server without the need to share real collected data. However, communication and computational costs for sharing model updates and performance are major bottlenecks for resource-constrained edge devices. This study introduces a representative-based parameter-sharing framework that aims to enhance the efficiency of federated learning in the Industry 4.0 environment. The framework begins with a server by distributing an initial model to edge devices, which then train it locally and send updated parameters back to the server for aggregation. To reduce communication and computational costs, the framework identifies groups of devices with similar parameter distributions and only sends updates from the resourceful and better-performing device, termed the cluster head, to the server. A backup cluster head is also elected to ensure reliability. Clustering is performed based on the device's parameter distributions and data characteristics. Moreover, the server incorporates randomly selected past aggregated parameters into the current aggregation process through weighted averaging where more recent parameters are given greater weight to enhance model performance. Comparative experimental evaluation with the state of the art using a testbed dataset demonstrates promising results by minimizing computational cost while preserving prediction performance, which ultimately enhances data analytics on edge devices in industrial environments.