When implementing hierarchical federated learning over wireless networks,
scalability assurance and the ability to handle both interference and device
data heterogeneity are crucial. This work introduces a learning method designed
to address these challenges, along with a scalable transmission scheme that
efficiently uses a single wireless resource through over-the-air computation.
To provide resistance against data heterogeneity, we employ gradient
aggregations. Meanwhile, the impact of interference is minimized through
optimized receiver normalizing factors. For this, we model a multi-cluster
wireless network using stochastic geometry, and characterize the mean squared
error of the aggregation estimations as a function of the network parameters.
We show that despite the interference and the data heterogeneity, the proposed
scheme achieves high learning accuracy and can significantly outperform the
conventional hierarchical algorithm.

这项研究提出了一种用于解决分层联邦学习在无线网络中可扩展性和干扰、设备数据异质性的挑战的学习方法，并通过优化的接收器归一化因子来减小干扰的影响。通过使用随机几何建模多簇无线网络并将聚合估计的均方误差作为网络参数的函数进行表征，论文证明了在干扰和数据异质性的情况下，所提出的方案实现了较高的学习准确性，并且可以显著优于传统的分层算法。