Federated Learning for Scalable Anomaly Detection and Pattern Discovery in IoT-Enabled Aquaponics Systems

This study introduces a federated learning-based architecture designed to support highly scalable and decentralized anomaly detection in IoT-integrated aquaponics systems. Emphasizing rigorous data privacy, the framework employs PrefixSpan for sequential pattern mining to extract significant temporal behaviors from heterogeneous distributed datasets. IoT sensors deployed across ۱۱ aquaponic ponds collected extensive datasets, each exceeding ۱۷۰,۰۰۰ entries, capturing vital indicators such as temperature, pH, turbidity, and fish growth metrics. The proposed FL model demonstrated strong correlations—exceeding ۰.۹—between water quality conditions and fish development, validating the system’s predictive robustness. Notably, Pond ۶ and Pond ۱۰ yielded ۱۲۶۹ and ۱۳۳۹ sequential patterns respectively, confirming the exceptional scalability of the model. The architecture also achieved a ۳۵% reduction in communication latency compared to conventional centralized systems, enabling responsive and efficient anomaly detection in real time. In parallel, a Top-k mining approach was employed to benchmark pattern interpretability as well as computational efficiency because it revealed trade-offs in sensitivity versus frequency-based simplification. Recent studies that focus upon aquaponics have also validated the operational superiority of the system in anomaly detection that is privacy-aware via comparison across models. The comparison highlighted its alignment to sustainable smart farming objectives. By addressing the limitations of centralized data handling, this framework offers a resilient, scalable, and privacy-aware approach to intelligent aquaponics management.

Keywords:

Aquaponics Systems , Internet of Things (IoT) , Federated Learning , Anomaly Detection , Sequential Pattern Mining