# Energy-Efficient Hierarchical Federated Anomaly Detection for the Internet of Underwater Things via Selective Cooperative Aggregation Source: [Energy-Efficient Hierarchical Federated Anomaly Detection for the Internet of Underwater Things via Selective Cooperative Aggregation]() Collection index: [llms.txt]() Data updated: 2026-03-28T05:00:04.914Z Authors: Kenechi Omeke, Michael Mollel, Lei Zhang, Qammer H. Abbasi, Muhammad Ali Imran Published: 2026-03-25T17:29:57.000Z ## Abstract Anomaly detection is a core service in the Internet of Underwater Things, yet training accurate distributed models underwater is difficult because acoustic links are low-bandwidth, energy-intensive, and often unable to support direct sensor-to-surface communication. Standard flat federated learning therefore faces two coupled limitations in underwater deployments: expensive long-range transmissions and reduced participation when only a subset of sensors can reach the gateway. This paper proposes an energy-efficient hierarchical federated learning framework for underwater anomaly detection based on three components: feasibility-aware sensor-to-fog association, compressed model-update transmission, and selective cooperative aggregation among fog nodes. The proposed three-tier architecture localises most communication within short-range clusters while activating fog-to-fog exchange only when smaller clusters can benefit from nearby larger neighbours. A physics-grounded underwater acoustic model is used to evaluate detection quality, communication energy, and network participation jointly. In large synthetic deployments, only about 48% of sensors can directly reach the gateway in the 200-sensor case, whereas hierarchical learning preserves full participation through feasible fog paths. Selective cooperation matches the detection accuracy of always-on inter-fog exchange while reducing its energy by 31-33%, and compressed uploads reduce total energy by 71-95% in matched sensitivity tests. Experiments on three real benchmarks further show that low-overhead hierarchical methods remain competitive in detection quality, while flat federated learning defines the minimum-energy operating point. These results provide practical design guidance for underwater deployments operating under severe acoustic communication constraints. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Detection on Synthetic IoUT deployment (test)](): Detection; Synthetic IoUT deployment (test); Participation Rate: 100. - [Anomaly Detection on SMD (PA-F1 and Energy Consumption)](): Anomaly Detection; SMD; PA-F1: 80.32. - [Anomaly Detection on SMAP (PA-F1, Energy)](): Anomaly Detection; SMAP; PA-F1: 72.36. - [Anomaly Detection on MSL (PA-F1 and Energy Consumption)](): Anomaly Detection; MSL; PA-F1: 87.27. ## Original sources and implementation [Original paper]() [PDF]()