In this work, we propose a communication-efficient two-layer federated
learning algorithm for distributed setups including a core server and multiple
edge servers with clusters of devices. Assuming different learning tasks,
clusters with a same task collaborate. To implement the algorithm over wireless
links, we propose a scalable clustered over-the-air aggregation scheme for the
uplink with a bandwidth-limited broadcast scheme for the downlink that requires
only two single resource blocks for each algorithm iteration, independent of
the number of edge servers and devices. This setup is faced with interference
of devices in the uplink and interference of edge servers in the downlink that
are to be modeled rigorously. We first develop a spatial model for the setup by
modeling devices as a Poisson cluster process over the edge servers and
quantify uplink and downlink error terms due to the interference. Accordingly,
we present a comprehensive mathematical approach to derive the convergence
bound for the proposed algorithm including any number of collaborating clusters
in the setup and provide important special cases and design remarks. Finally,
we show that despite the interference in the proposed uplink and downlink
schemes, the proposed algorithm achieves high learning accuracy for a variety
of parameters.

本文提出了一种基于聚类的两层联邦学习算法，使用可扩展的聚类无线聚合方案实现算法的上行传输，使用适用于带宽受限的广播方案进行下行传输，展示了在干扰环境下该方法的高精度学习表现并导出该方法的收敛界。