With the rapid proliferation of Internet of Things (IoT) devices and the growing concern for data privacy among the public, Federated Learning (FL) has gained significant attention as a privacy-preserving machine learning paradigm. FL enables the training of a global model among clients without exposing local data. However, when a federated learning system runs on wireless communication networks, limited wireless resources, heterogeneity of clients, and network transmission failures affect its performance and accuracy. In this study, we propose a novel dynamic cross-tier FL scheme, named FedDCT to increase training accuracy and performance in wireless communication networks. We utilize a tiering algorithm that dynamically divides clients into different tiers according to specific indicators and assigns specific timeout thresholds to each tier to reduce the training time required. To improve the accuracy of the model without increasing the training time, we introduce a cross-tier client selection algorithm that can effectively select the tiers and participants. Simulation experiments show that our scheme can make the model converge faster and achieve a higher accuracy in wireless communication networks.

翻译：随着物联网设备的快速普及以及公众对数据隐私问题的日益关注，联邦学习作为一种隐私保护的机器学习范式受到了广泛关注。联邦学习能够在客户端之间训练全局模型，同时无需暴露本地数据。然而，当联邦学习系统在无线通信网络上运行时，有限的无线资源、客户端的异构性以及网络传输故障会影响其性能与精度。在本研究中，我们提出了一种新颖的动态跨层联邦学习方案，命名为FedDCT，旨在提升无线通信网络中的训练精度与性能。我们利用一种分层算法，根据特定指标将客户端动态划分为不同层级，并为每个层级分配特定的超时阈值以减少所需训练时间。为了在不增加训练时间的前提下提高模型精度，我们引入了一种跨层客户端选择算法，该算法能够有效选择层级与参与者。仿真实验表明，我们的方案能够使模型更快收敛，并在无线通信网络中达到更高的精度。