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

Kenechi Omeke, Michael Mollel, Lei Zhang, Qammer H. Abbasi, Muhammad Ali Imran

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.

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

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.

Paper Structure

This paper contains 40 sections, 27 equations, 8 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. IoUT Architectures, Anomaly Detection, and Edge Intelligence
  4. Federated and Hierarchical Federated Learning
  5. Communication- and Energy-Efficient Federated Optimisation
  6. Summary of Gaps and Positioning
  7. System Model
  8. Network Architecture
  9. Underwater Acoustic Propagation
  10. Ambient Noise and Receiver SNR
  11. SNR-Driven Energy Model
  12. Sensing and Data Model
  13. System Setup Illustration and Parameters
  14. Problem Formulation
  15. Learning Objective
  16. ...and 25 more sections

Figures (8)

  • Figure 1: Proposed three-tier HFL architecture for IoUT with feasibility-aware association, compressed uplinks, and selective fog cooperation.
  • Figure 2: Stratified IoUT system model. Sensors (deep layer) transmit local model updates to fog aggregators (mid-water); fog nodes cooperatively exchange partial aggregates (dashed) and forward to the surface gateway.
  • Figure 3: Bi-level view of the framework: hierarchical FL training (top) and the decision-rule layer that selects sensor associations and fog cooperation patterns (bottom).
  • Figure 4: Convergence behaviour of the synthetic method set used in the main scalability study. (a) Training loss at $N{=}150$. (b) Training loss at $N{=}200$. Across both scales, the loss curves flatten by roughly rounds 10--12, supporting the choice $T{=}20$ for the main synthetic experiments. Error bands denote one standard deviation over three seeds.
  • Figure 5: Participation and trade-off results in synthetic IoUT deployments. (a) Direct gateway reachability decreases with scale, while fog-assisted reachability remains near-complete. (b) Detection quality as a function of network scale. (c) Per-sensor communication energy as a function of network scale. Error bars denote one standard deviation over three seeds.
  • ...and 3 more figures