MQTT-Enabled Federated Self-Learning Minimal Learning Machine Classifier for Real-Time Hyperspectral Processing

Abstract. Despite its potential in forestry, agriculture, environmental monitoring, safety surveillance, and defence, real-time hyperspectral imaging (HSI) remains challenging in practice because of the high dimensionality of the data and limited onboard computational resources. This work introduces a distributed HSI classification framework that integrates federated learning, a Self-learning Minimal Learning Machine classifier (SL-MLM), adaptive Kalman filter-based model fusion, and lightweight MQTT-based communication on Raspberry Pi edge devices and a laptop serving as the base station. Acting as local nodes, Raspberry Pis process HSI data row by row, update their models recursively, and only exchange compact model parameters and classification results with the base station. HSI data in its raw form remains local. The findings suggest that the proposed local learning workflow can be implemented on Raspberry Pi devices, and Kalman-based fusion improves stability and consistency in comparison to individual local models. The method is feasible in scenarios where the number of labelled data points is restricted, as the SL-MLM classifier can be initialized with a mere handful of class-specific reference points. The research demonstrates a feasible, low-cost approach to distributed embedded HSI classification and sensing.