Client-Edge-Cloud Hierarchical Federated Learning

• Published in: ICC 2020 - 2020 IEEE International Conference on Communications (ICC) pp. 1 - 6
• Main Authors: Liu, Lumin, Zhang, Jun, Song, S.H., Letaief, Khaled B.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2020
• Subjects: Cloud computing; Computational modeling; Convergence; Data models; Edge Learning; Federated Learning; Machine learning; Mobile Edge Computing; Servers; Training
• ISSN: 1938-1883
• DOI: 10.1109/ICC40277.2020.9148862

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.