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

arXiv 2408.10024 ↗First posted 19 Aug 2024cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 27 references that are in the map. First posted on arXiv 19 Aug 2024.

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References · 24

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12 more
  1. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  2. 2019Federated Machine Learning: Concept and Applicationscited by 412
  3. 2020Threats to Federated Learning: A Surveycited by 178
  4. 2019SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentumcited by 121
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  7. 2022Application of federated learning in manufacturingcited by 11
  8. 2024Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detectioncited by 9
  9. 2023Federated Ensemble YOLOv5 -- A Better Generalized Object Detection Algorithmcited by 4
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  11. 2023Federated Object Detection for Quality Inspection in Shared Productioncited by 3
  12. 2024Seamless Integration: Sampling Strategies in Federated Learning Systemscited by 1

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