Energy-Efficient Hierarchical Federated Anomaly Detection for the Internet of Underwater Things via Selective Cooperative Aggregation

2026-03-25

AI Summary

This work addresses the challenges of high communication overhead and low device participation in conventional flat federated learning for anomaly detection in underwater Internet of Things (IoT), where acoustic links suffer from limited bandwidth and high energy consumption. To overcome these limitations, the authors propose a three-tier hierarchical federated learning architecture that leverages the characteristics of acoustic propagation to enable feasibility-aware association between sensors and fog nodes. The framework incorporates model compression to reduce uplink energy costs and introduces a selective collaborative aggregation mechanism that triggers inter-fog communication only when necessary. Experimental results on 200-node simulations demonstrate 100% device participation—significantly outperforming the 48% achieved by flat approaches—along with 71–95% reduction in total energy consumption due to compressed uploads and 31–33% savings in inter-fog communication energy, while maintaining high detection accuracy across three real-world datasets.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Federated Learning
• Anomaly Detection
• Internet of Underwater Things
• Energy Efficiency
• Acoustic Communication

Innovation

• Hierarchical Federated Learning
• Underwater Acoustic Communication
• Selective Cooperative Aggregation
• Model Compression
• Energy-Efficient Anomaly Detection