Federated Learning is a collaborative machine learning framework to train a
deep learning model without accessing clients' private data. Previous works
assume one central parameter server either at the cloud or at the edge. The
cloud server can access more data but with excessive communication overhead and
long latency, while the edge server enjoys more efficient communications with
the clients. To combine their advantages, we propose a client-edge-cloud
hierarchical Federated Learning system, supported with a HierFAVG algorithm
that allows multiple edge servers to perform partial model aggregation. In this
way, the model can be trained faster and better communication-computation
trade-offs can be achieved. Convergence analysis is provided for HierFAVG and
the effects of key parameters are also investigated, which lead to qualitative
design guidelines. Empirical experiments verify the analysis and demonstrate
the benefits of this hierarchical architecture in different data distribution
scenarios. Particularly, it is shown that by introducing the intermediate edge
servers, the model training time and the energy consumption of the end devices
can be simultaneously reduced compared to cloud-based Federated Learning.

该研究提出了一种基于层次化客户端边缘云联合学习的系统，并支持使用 HierFAVG 算法对多个边缘服务器进行部分模型聚合以适应不同的数据分布情况和优化通信与计算之间的权衡，同时验证了这种结构在能够减少模型训练时间和末端设备的能量消耗。