Client-Edge-Cloud Hierarchical Federated Learning

TL;DR

This paper introduces a hierarchical federated learning system combining client, edge, and cloud servers to improve training efficiency and reduce communication costs, supported by a new aggregation algorithm and empirical validation.

Contribution

It proposes a novel client-edge-cloud hierarchical federated learning framework with the HierFAVG algorithm for partial model aggregation, enhancing training speed and communication efficiency.

Findings

Reduced training time with hierarchical architecture

Lower energy consumption on end devices

Effective in various data distribution scenarios

Abstract

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.