Federated Learning-Enabled Collaborative Intelligence for Energy-Constrained Underwater Sensor Networks in Naval Surveillance Systems

Underwater wireless sensor networks (UWSNs) are essential to the work of the navy, as they are used to monitor objects (surveillance), to monitor the environment (environmental monitoring), and to defend the tactics (tactical defense). They are however challenged by serious issues in deployment because of limitation of underwater communication by acoustic means such as high latency, low bandwidth, high rate of packet loss and extreme energy limitation. The conventional centralized approach of data processing cannot survive under these circumstances and a shift towards the decentralized intelligence is needed. This paper will present an Energy-Aware Clustered Federated Learning (CFL) framework, which is specific to UWSNs in naval systems. The approach suggested will arrange sensor nodes into logical cluster, where local models are being trained and aggregated at cluster heads and sent to a central unit. In order to extend the network lifetime, an energy-conscious participation scheme is used to make sure that only nodes that are energy-reliant participate in model training. Moreover, we propose a powerful median-based aggregation approach at the cluster level in order to overcome the impacts of underwater communications that are noisy and lossy. Simulations conducted under realistic conditions in the underwater environment prove that the proposed CFL architecture is much more accurate in models, can boost communication overhead, and increase the energy efficiency of the system as opposed to conventional federated learning tools. It is also demonstrated that the results are much more robust to packet loss and communication failures, confirming the relevance of the framework in autonomous underwater operations. This paper points out the potential transformations that federated learning can bring to allow the development of intelligent, resilient, and energy-efficient, underwater sensor networks, and create new opportunities in the future in the fields of naval and maritime applications in challenging underwater conditions.                  Keywords: Underwater Wireless Sensor Networks (UWSNs); Federated Learning; Energy Aware Systems; Clustered Aggregation; Naval Applications.