Abstract: Federated learning, as a distributed learning approach that does not require centralizing raw data, demonstrates significant potential in collaborative sensing and decision-making for underwater autonomous vehicle fleets. However, challenges in underwater communication environments, such as severe acoustic channel fading and limited communication bandwidth, cause traditional federated learning methods to suffer from reduced aggregation accuracy and excessive energy consumption, making them inadequate for long-term tasks and battery-powered devices. To address these issues, this paper proposes an IRS-assisted underwater federated learning joint optimization framework (IRS-JOFL). By introducing intelligent reflecting surfaces (IRS) and aerial computation mechanisms, the framework enhances uplink quality and improves gradient aggregation efficiency, while jointly optimizing device selection and power control strategies. This approach ensures model accuracy while significantly reducing communication energy consumption. Experimental results show that, on the Fashion-MNIST dataset, IRS-JOFL achieves an accuracy of 86.73%, which is an improvement of about 5.4% and 3.6% compared to traditional FedAvg and Air-FL without IRS, respectively, while reducing total energy consumption by approximately 16.3% and 14.1%. On the Fish dataset, the proposed scheme achieves a final Top-1 accuracy of approximately 86.6% and maintains the lowest energy consumption when reaching the 80% accuracy threshold.

摘要： 联邦学习作为一种无需集中原始数据的分布式学习方式，在水下自主航行器群体协同感知与决策中展现出重要潜力。然而，水下通信环境的挑战，如剧烈的水声信道衰落和有限的通信带宽，使得传统联邦学习在水下场景中面临聚合精度降低和能量开销过大的问题，难以满足长期任务和电池供电设备的需求。为此，本文提出一种智能反射面辅助的水下联邦学习联合优化框架（IRS-JOFL），该方案通过引入IRS和空中计算机制，增强上行链路质量并提升梯度聚合效率，同时联合优化设备选择与功率控制策略，既能保证模型精度，又能显著降低通信能耗。实验结果表明，在Fashion-MNIST数据集上，IRS-JOFL的准确率为86.73%，相比传统 FedAvg和未引入IRS的Air-FL方案精度提升约5.4%和3.6%，同时总能耗降低约16.3%和14.1%。在Fish数据集上，所提方案的最终Top-1精度约为86.6%，并在达到80%精度阈值时保持最低能耗。