A Semantic-Oriented Federated Learning for Hybrid Ground–Aqua Computing Systems

Nowadays, an ambitious target of the next-generation networks is to develop intelligent overarching space–air–ground–aqua computing systems, in order to provide a smart ecosystem able to efficiently operate computation in heterogeneous domains. In particular, in such a context, the underwater environment requires a special attention, since it is recognized as the most challenging domain, due to channel impairments and adverse propagation conditions. This article proposes a self-intelligent system able to efficiently perform underwater environment monitoring or underwater survey of critical infrastructure, by resorting to the use of the semantic communication paradigm to lower the impairments due to the underwater channel propagation conditions. In particular, in our case, images sent by underwater devices are collected by shore small base stations (SSBSs) to form their training data set to take part in a federated learning (FL) process with a ground base station. In particular, this article considers a semantic communication scheme based on a deep-convolution neural networks encoder–decoder architecture for an efficient exploitation of the data transmission from underwater devices to the linked SSBSs. Performance analysis is provided to show better behavior of the proposed system in comparison with the conventional alternative that does not involve the use of the semantic communication approach. Finally, a specific performance evaluation analysis is devoted to the investigation of the convergence behavior of the proposed FL procedure in reference to the cross ground–aqua system considered in order to highlight its advantages with respect to a classical implementation.