In a real federated learning (FL) system, communication overhead for passing model parameters between the clients and the parameter server (PS) is often a bottleneck. Hierarchical federated learning (HFL) that poses multiple edge servers (ESs) between clients and the PS can partially alleviate communication pressure but still needs the aggregation of model parameters from multiple ESs at the PS. To further reduce communication overhead, we bring sequential FL (SFL) into HFL for the first time, which removes the central PS and enables the model training to be completed only through passing the global model between two adjacent ESs for each iteration, and propose a novel algorithm adaptive to such a combinational framework, referred to as Fed-CHS. Convergence results are derived for strongly convex and non-convex loss functions under various data heterogeneity setups, which show comparable convergence performance with the algorithms for HFL or SFL solely. Experimental results provide evidence of the superiority of our proposed Fed-CHS on both communication overhead saving and test accuracy over baseline methods.

翻译：在实际的联邦学习系统中，客户端与参数服务器之间传递模型参数所产生的通信开销往往是性能瓶颈。通过在客户端与参数服务器之间引入多个边缘服务器构成的分层联邦学习架构，可以部分缓解通信压力，但仍需在参数服务器处聚合来自多个边缘服务器的模型参数。为了进一步降低通信开销，我们首次将顺序联邦学习引入分层架构，该方法移除了中心参数服务器，使得模型训练在每次迭代中仅需通过相邻边缘服务器之间传递全局模型即可完成，并提出了一种适应于此组合框架的新算法，称为Fed-CHS。我们在多种数据异构设置下，针对强凸和非凸损失函数推导了收敛性结果，表明其收敛性能与单独使用分层联邦学习或顺序联邦学习的算法相当。实验结果证明，相较于基线方法，我们提出的Fed-CHS在通信开销节省和测试准确率方面均具有优越性。