Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing

• URL: http://arxiv.org/abs/2408.10024v3
• Date: Tue, 8 Oct 2024 11:51:00 GMT
• Title: Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing
• Authors: Vinit Hegiste, Tatjana Legler, Martin Ruskowski,
• Abstract summary: This study investigates the comparative effectiveness of two weight selection strategies: Final Epoch Weight Selection (FEWS) and Optimal Epoch Weight Selection (OEWS) We employ various neural network architectures, including EfficientNet, ResNet, and VGG, to assess the impact of these weight selection strategies on model convergence and robustness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the realm of Federated Learning (FL), particularly within the manufacturing sector, the strategy for selecting client weights for server aggregation is pivotal for model performance. This study investigates the comparative effectiveness of two weight selection strategies: Final Epoch Weight Selection (FEWS) and Optimal Epoch Weight Selection (OEWS). Designed for manufacturing contexts where collaboration typically involves a limited number of partners (two to four clients), our research focuses on federated image classification tasks. We employ various neural network architectures, including EfficientNet, ResNet, and VGG, to assess the impact of these weight selection strategies on model convergence and robustness. Our research aims to determine whether FEWS or OEWS enhances the global FL model's performance across communication rounds (CRs). Through empirical analysis and rigorous experimentation, we seek to provide valuable insights for optimizing FL implementations in manufacturing, ensuring that collaborative efforts yield the most effective and reliable models with a limited number of participating clients. The findings from this study are expected to refine FL practices significantly in manufacturing, thereby enhancing the efficiency and performance of collaborative machine learning endeavors in this vital sector.

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