Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism

TL;DR

This paper explores federated learning at network edges for IoT devices, focusing on resource optimization and incentive mechanisms modeled through game theory, and discusses open challenges and future directions.

Contribution

It introduces a Stackelberg game model for incentivizing IoT devices in federated learning and discusses key design aspects and open research challenges.

Findings

Modeling incentives with Stackelberg game enhances device participation.

Identifies key design considerations for edge federated learning.

Outlines open challenges and potential solutions for future research.

Abstract

Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices. IoT devices with intelligence require the use of effective machine learning paradigms. Federated learning can be a promising solution for enabling IoT-based smart applications. In this paper, we present the primary design aspects for enabling federated learning at network edge. We model the incentive-based interaction between a global server and participating devices for federated learning via a Stackelberg game to motivate the participation of the devices in the federated learning process. We present several open research challenges with their possible solutions. Finally, we provide an outlook on future research.