A Workflow Management System Approach To Federated Learning: Application to Industry 4.0

被引：0

作者：

Safri, Hamza ^{[1
,2
]}

Papadimitriou, George ^{[3
]}

Desprez, Frederic ^{[4
]}

Deelman, Ewa ^{[3
]}

机构：

[1] Berger Levrault, Toulouse, France

[2] Grenoble Univ, Grenoble, France

[3] Univ Southern Calif, Los Angeles, CA USA

[4] INRIA, Grenoble, France

来源：

2024 20TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SMART SYSTEMS AND THE INTERNET OF THINGS, DCOSS-IOT 2024 | 2024年

关键词：

Federated learning; IoT; Industrial Internet of Things; workflows management system;

D O I：

10.1109/DCOSS-IoT61029.2024.00047

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Federated Learning (FL) combined with the Industrial Internet of Things (IIoT) enhances decision-making in industrial settings by leveraging decentralized machine learning (ML) to ensure data privacy, optimize edge computing, and facilitate adaptive model training. However, implementing FL and IIoT presents challenges due to distributed architectures, including communication, data transfer, and file management across wide area networks. This paper addresses these challenges by introducing FL and Clustered FL (CFL) models using Pegasus workflows. Evaluated with real data from airport baggage conveyor systems, it offers practical insights into FL's application in IIoT environments, contributing to advancements in intelligent industrial decision-making.

引用

页码：259 / 263

页数：5

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