Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism

2019/11/05 by Latif U. Khan, Shashi Raj Pandey, Khan, Latif U. +11 · 4 citations
Computer Science · Social Sciences · #Cooperative Communication and Network Coding #Distributed #FOS: Computer and information sciences #Parallel #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #and Cluster Computing (cs.DC) #cs.DC

paper · pdf · doi:10.48550/arxiv.1911.05642

The first two authors contributed equally. This article has been accepted for publication in IEEE Communications Magazine

openalex publication_date 2019/11/05 · arxiv created 2020/09/07 · arxiv updated 2020/09/08 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28

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.

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• Incentivizing High-quality Participation From Federated Learning Agents
• Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection

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