The application of federated learning (FL) in industrial settings offers promising advancements in maintaining data privacy while collaboratively training machine learning models. This study focuses on the comparative analysis of federated image classification versus locally trained models within a shared production environment. Specifically, we explore the classification of windshields in truck cabins, which is a crucial task for quality inspection in manufacturing of trucks. Our research involves four clients, each producing different types of truck cabins and research based on FL process between them. Various deep learning architectures, including VGG19, ResNet50, InceptionNetv3, DenseNet-121, and EfficientNetv2-s, were evaluated under a FL framework implemented using the FLOWER framework. A custom plain averaging strategy was used for weight aggregation. The global model’s performance was assessed using a combined test set from all clients and compared against models trained locally by individual clients. The results highlight the effectiveness of FL in enhancing model generalization and adaptability to new product variations in industrial applications, promoting its adoption for collaborative quality inspection tasks.


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    Title :

    Collaborative Learning in Shared Production Environment Using Federated Image Classification


    Additional title:

    Lect.Notes Mechanical Engineering



    Conference:

    European Symposium on Artificial Intelligence in Manufacturing ; 2024 ; Athens, Greece October 16, 2024 - October 16, 2024



    Publication date :

    2025-03-22


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English