Federated Learning for Internet of Underwater Things Based on Lightweight Distillation and Data Refinement

Abstract

Underwater federated learning (UFL) is an emerging technology to realize distributed intelligent collaboration in the Internet of Underwater Things (IoUT), but its application faces two challenges: the limited bandwidth of underwater communication leads to low model transmission efficiency, and the data is characterized by low quality and high heterogeneity due to environmental interference. In this paper, an underwater federated learning framework with dual-path collaborative optimization is proposed to solve the above problems systematically through the joint design of knowledge distillation and data quality enhancement. Specifically, to optimize the transmission efficiency, a knowledge distillation mechanism is designed, and the complex model is compressed into a simplified model suitable for low-bandwidth transmission by using the collaborative distillation of lightweight teacher-student models. To enhance data quality, a supervised data quality enhancement (S-DQE) method is proposed. The integration of traditional methods with deep learning-based approaches optimizes feature representation through the joint application of contrastive learning and adversarial training, thereby effectively addressing the issue of low-quality underwater data. Finally, numerical results are given to compare the final scheme with the initial federated learning scheme, lightweight model scheme, and lightweight-data quality enhancement scheme, clearly demonstrating its performance gains.