Federated learning (FL), which addresses data privacy issues by training models on resource-constrained mobile devices in a distributed manner, has attracted significant research attention. However, the problem of optimizing FL client selection in mobile federated learning networks (MFLNs), where devices move in and out of each others' coverage and no FL server knows all the data owners, remains open. To bridge this gap, we propose a first-of-its-kind \underline{Soc}ially-aware \underline{Fed}erated \underline{C}lient \underline{S}election (SocFedCS) approach to minimize costs and train high-quality FL models. SocFedCS enriches the candidate FL client pool by enabling data owners to propagate FL task information through their local networks of trust, even as devices are moving into and out of each others' coverage. Based on Lyapunov optimization, we first transform this time-coupled problem into a step-by-step optimization problem. Then, we design a method based on alternating minimization and self-adaptive global best harmony search to solve this mixed-integer optimization problem. Extensive experiments comparing SocFedCS against five state-of-the-art approaches based on four real-world multimedia datasets demonstrate that it achieves 2.06\% higher test accuracy and 12.24\% lower cost on average than the best-performing baseline.

翻译：联邦学习（FL）通过在资源受限的移动设备上以分布式方式训练模型，解决了数据隐私问题，已吸引了大量研究关注。然而，在移动联邦学习网络（MFLNs）中优化FL客户端选择的问题——设备会相互进入或离开覆盖范围，且没有FL服务器知晓所有数据所有者——仍未得到解决。为填补这一空白，我们首次提出了一种社交感知的联邦客户端选择方法（SocFedCS），旨在最小化成本并训练高质量的FL模型。SocFedCS通过允许数据所有者在其本地信任网络中传播FL任务信息（即使设备在相互进入或离开覆盖范围时），丰富了候选FL客户端池。基于李雅普诺夫优化，我们首先将这个时间耦合问题转化为逐步优化问题。然后，我们设计了一种基于交替最小化和自适应全局最佳和声搜索的方法来求解这个混合整数优化问题。通过将SocFedCS与五种基于四个真实多媒体数据集的最先进方法进行广泛实验对比，结果表明，与性能最佳的基线相比，其平均测试准确率提高了2.06%，成本降低了12.24%。