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.

翻译：随着无线设备到设备（D2D）网络中智能移动设备的普及，去中心化联邦学习（DFL）受到了广泛关注。与集中式联邦学习（CFL）相比，DFL降低了因通信瓶颈导致中央服务器故障的风险。然而，DFL面临若干挑战，例如不同环境中数据分布的严重异构性，以及在D2D网络中采用用户数据报协议（UDP）导致的传输中断和报文错误。这些挑战往往使DFL模型的训练收敛性变差。针对这些问题，我们对DFL进行了深入的理论收敛性分析，并推导出收敛界。通过在该收敛界中定义一个名为“不可靠链路感知邻域差异”的新颖量，我们构建了一个可处理的优化目标，并提出了一种考虑DFL中表征差异与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两种设置下的大量实验验证了我们所提方法的有效性，结果表明其收敛速度和测试准确率均有提升，且与理论分析一致。