The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server. FedL ignores two important characteristics of contemporary wireless networks, however: (i) the network may contain heterogeneous communication/computation resources, and (ii) there may be significant overlaps in devices' local data distributions. In this work, we develop a novel optimization methodology that jointly accounts for these factors via intelligent device sampling complemented by device-to-device (D2D) offloading. Our optimization methodology aims to select the best combination of sampled nodes and data offloading configuration to maximize FedL training accuracy while minimizing data processing and D2D communication resource consumption subject to realistic constraints on the network topology and device capabilities. Theoretical analysis of the D2D offloading subproblem leads to new FedL convergence bounds and an efficient sequential convex optimizer. Using these results, we develop a sampling methodology based on graph convolutional networks (GCNs) which learns the relationship between network attributes, sampled nodes, and D2D data offloading to maximize FedL accuracy. Through evaluation on popular datasets and real-world network measurements from our edge testbed, we find that our methodology outperforms popular device sampling methodologies from literature in terms of ML model performance, data processing overhead, and energy consumption.

翻译：传统联邦学习（FedL）架构通过让工作设备训练本地模型并定期由服务器聚合，将机器学习（ML）分布到各设备中。然而，FedL忽略了当代无线网络的两个重要特征：（i）网络可能包含异构的计算/通信资源，（ii）设备本地数据分布可能存在显著重叠。本文提出一种新型优化方法，通过智能设备采样与设备到设备（D2D）卸载相配合，联合考虑上述因素。我们的优化方法旨在选择最优的采样节点组合与数据卸载配置，在满足网络拓扑和设备能力的实际约束下，最大化FedL训练精度，同时最小化数据处理与D2D通信资源消耗。对D2D卸载子问题的理论分析导出了新的FedL收敛界及高效的序列凸优化器。基于这些结果，我们开发了一种基于图卷积网络（GCN）的采样方法，该方法学习网络属性、采样节点与D2D数据卸载之间的关系以最大化FedL精度。通过在主流数据集和边缘测试床的真实网络测量数据上的评估，我们发现我们的方法在机器学习模型性能、数据处理开销和能耗方面均优于文献中流行的设备采样方法。