Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

04/21/2023

∙

Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning model without compromising the privacy and confidentiality of their data. In this paper, we evaluate the performance of different FL aggregation methods and compare them to central and local training approaches. Our study is based on four datasets with varying data distributions. The results indicate that the performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting.

READ FULL TEXT

Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

Federated Object Detection for Quality Inspection in Shared Production

Loosely Coupled Federated Learning Over Generative Models

Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting

martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture

Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning

A Federated Channel Modeling System using Generative Neural Networks

Zero-Knowledge Proof-based Practical Federated Learning on Blockchain

Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

Related Research

Federated Object Detection for Quality Inspection in Shared Production

Loosely Coupled Federated Learning Over Generative Models

Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting

martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture

Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning

A Federated Channel Modeling System using Generative Neural Networks

Zero-Knowledge Proof-based Practical Federated Learning on Blockchain