Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets. As FL does not collect and store the data centrally, it requires frequent model exchange through the wireless network. However, since the aggregation in FL can be partially participated with synchronized frequency, its communication pattern is different from the conventional network. Therein, limited bandwidth and package loss restrict interactions in training. Thus, the network scheduling could largely affect the FL convergence. To figure out the specific effects, we analyze the convergence rate of FL regarding the joint impact of communication and training. Combining it with the network model, we formulate the optimal scheduling problem for FL implementation. The theoretical results could guide the hyper-parameter design in the network and explain the principle of how the wireless communication could influence the FL training process.

中文翻译：

---

无线网络联邦学习的收敛性分析和系统设计


联邦学习（FL）最近成为物联网中一种重要且有前途的学习方案，使设备能够在不共享原始数据集的情况下共同学习模型。由于FL不集中收集和存储数据，因此需要通过无线网络进行频繁的模型交换。然而，由于FL中的聚合可以以同步频率部分参与，因此其通信模式与传统网络不同。其中，有限的带宽和丢包限制了训练中的交互。因此，网络调度很大程度上影响FL收敛。为了弄清楚具体效果，我们分析了 FL 在沟通和训练的联合影响方面的收敛速度。结合网络模型，我们制定了 FL 实现的最优调度问题。理论结果可以指导网络中的超参数设计，并解释无线通信如何影响FL训练过程的原理。