Communication-Efficient Semihierarchical Federated Analytics in IoT Networks

Abstract

The convergence of the Internet of Things (IoT) and data analytics has great potential to accelerate knowledge discovery, while the traditional approach of centralized data collection then processing is becoming infeasible in many applications due to efficiency and privacy concerns. Federated learning (FL) has emerged as a new paradigm that enables model learning across distributed IoT devices without sharing raw data. However, previous works on FL are either relying on a single central server or fully decentralized. In this article, we propose a semihierarchical federated analytics framework combining the advantages of the above architectures. The proposed framework leverages multiple edge servers for aggregating updates from IoT devices and fusing learned model weights without the need of cloud or a central server. Besides, we develop a new local client update rule to further improve the communication efficiency by reducing the communication rounds between IoT devices and edge servers. We analyze the convergence properties of the presenting approach and investigate its characteristics considering the effects of varying parameters, unreliable links, and packet loss. The experimental results demonstrate the effectiveness of our proposed methodology in providing communication-efficient, robust, and fault-tolerant data analytics to IoT networks.