Federated Learning

Federated learning is a distributed data analysis approach used in many IoT applications, including IoMT, due to its ability to provide acceptable accuracy and privacy. However, a critical issue with Federated learning is the poisoning attack, which has severe consequences on the accuracy of the global model caused by the server's lack of access to raw data. To deal with this problem effectively, a distributed federated learning approach involving blockchain technology is proposed. Using the consensus mechanism based on reputation-based verifier selection, verifiers are selected based on their honest participation in identifying compromised clients. This approach ensures that these clients are correctly identified and their attack is ineffective. The proposed detection mechanism can efficiently resist the data poisoning attack, which significantly improves the accuracy of the global model. Based on evaluation, the accuracy of the global model is compared with and without the proposed detection mechanism that varies with the percentage of poisonous clients and different values for the fraction of poisonous data. In addition to the stable accuracy range of nearly 93%, the accuracy of our proposed detection mechanism is not affected by the increase of α in different values of β.

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 11 aquaponic ponds collected extensive datasets, each exceeding 170,000 entries, capturing vital indicators such as temperature, pH, turbidity, and fish growth metrics. The proposed FL model demonstrated strong correlations—exceeding 0.9—between water quality conditions and fish development, validating the system’s predictive robustness. Notably, Pond 6 and Pond 10 yielded 1269 and 1339 sequential patterns respectively, confirming the exceptional scalability of the model. The architecture also achieved a 35% 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.