With the proliferation of intelligent mobile devices in wireless
device-to-device (D2D) networks, decentralized federated learning (DFL) has
attracted significant interest. Compared to centralized federated learning
(CFL), DFL mitigates the risk of central server failures due to communication
bottlenecks. However, DFL faces several challenges, such as the severe
heterogeneity of data distributions in diverse environments, and the
transmission outages and package errors caused by the adoption of the User
Datagram Protocol (UDP) in D2D networks. These challenges often degrade the
convergence of training DFL models. To address these challenges, we conduct a
thorough theoretical convergence analysis for DFL and derive a convergence
bound. By defining a novel quantity named unreliable links-aware neighborhood
discrepancy in this convergence bound, we formulate a tractable optimization
objective, and develop a novel Topology Learning method considering the
Representation Discrepancy and Unreliable Links in DFL, named ToLRDUL.
Intensive experiments under both feature skew and label skew settings have
validated the effectiveness of our proposed method, demonstrating improved
convergence speed and test accuracy, consistent with our theoretical findings.

在无线设备到设备（D2D）网络中，去中心化联邦学习（DFL）作为智能移动设备普及的产物引起了极大的兴趣。DFL 相对于集中式联邦学习（CFL）降低了由于通信瓶颈而导致的中央服务器故障的风险。然而，DFL 面临着许多挑战，如多样环境中数据分布的严重异质性，以及 D2D 网络中采用用户数据报协议（UDP）导致的传输中断和包错误等。为了解决这些挑战，我们对 DFL 进行了全面的理论收敛性分析，并推导出一个收敛限。在该收敛限中，我们定义了一种称为不可靠链路感知邻域差异的新颖量，并制定了一个可解的优化目标，开发了一种考虑 DFL 中表示差异和不可靠链路的拓扑学习方法，命名为 ToLRDUL。在特征偏斜和标签偏斜的情况下进行了大量实验验证了我们提出方法的有效性，实验结果与我们的理论发现相一致，显示出了提高收敛速度和测试精度。