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FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection

Published: 12 June 2024 Publication History

Abstract

Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models without sharing local data with other parties, hence preserving data privacy. Nevertheless, when implementing FL in Industrial visual inspection (IVI), the constraints posed by limited data availability and the intricate nature of the inspection tasks significantly impact the performance of the resulting model. This paper introduces FedTR, a novel FL framework incorporating transfer learning designed for Autonomous IVI, focusing on the challenging task of identifying label defects through end-to-end text recognition. Transfer learning is a method that leverages the knowledge of a pre-trained model to adapt to a different dataset. FedTR initially trains the model using a publicly available dataset, after which performs the essential federated learning process with model fine-tuning on the distributed and limited private data. Extensive experiment results demonstrate the effectiveness and feasibility of FedTR on private ink cartridge datasets for label defect identification. FedTR achieves an end-to-end text recognition word-level accuracy of 95.5% and 94.2% on homogeneous and heterogeneous data respectively. Additionally, it attains performance levels that are on par with those achieved through centralized training.

References

[1]
Jeonghun Baek 2019. What is wrong with scene text recognition model comparisons? dataset and model analysis. In Proceedings of the IEEE/CVF international conference on computer vision. 4715–4723.
[2]
Warren B Chik. 2013. The Singapore Personal Data Protection Act and an assessment of future trends in data privacy reform. Computer Law & Security Review 29, 5 (2013), 554–575.
[3]
Hee-Jae Cho and Vladimir Pucik. 2005. Relationship between innovativeness, quality, growth, profitability, and market value. Strategic management journal 26, 6 (2005), 555–575.
[4]
Ankush Gupta 2016. Synthetic Data for Text Localisation in Natural Images. In IEEE Conference on Computer Vision and Pattern Recognition.
[5]
Shuo Huai 2022. Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems. In 2022 IEEE 40th International Conference on Computer Design (ICCD). IEEE, 627–634.
[6]
Qinbin Li 2021. A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering (2021).
[7]
Tian Li 2020. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems 2 (2020), 429–450.
[8]
Brendan McMahan 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR, 1273–1282.
[9]
Lisa Torrey and Jude Shavlik. 2010. Transfer learning. In Handbook of research on machine learning applications and trends: algorithms, methods, and techniques. IGI global, 242–264.
[10]
Paul Voigt and Axel Von dem Bussche. 2017. The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing 10, 3152676 (2017), 10–5555.
[11]
Chien-Yao Wang 2022. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696 (2022).
[12]
Marcus Yung 2020. Examining the fatigue-quality relationship in manufacturing. Applied Ergonomics 82 (2020), 102919.
[13]
Fuzhen Zhuang 2020. A comprehensive survey on transfer learning. Proc. IEEE 109, 1 (2020), 43–76.

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  1. FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection

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      Published In

      cover image ACM Conferences
      GLSVLSI '24: Proceedings of the Great Lakes Symposium on VLSI 2024
      June 2024
      797 pages
      ISBN:9798400706059
      DOI:10.1145/3649476
      This work is licensed under a Creative Commons Attribution International 4.0 License.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 12 June 2024

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      Author Tags

      1. Federated Learning
      2. Industrial Visual Inspection
      3. Transfer Learning

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      • Short-paper
      • Research
      • Refereed limited

      Funding Sources

      • the RIE2020 Industry Alignment Fund ? Industry Collaboration Projects (IAF-ICP) Funding Initiative
      • Academic Research Fund Tier 1 (RG94/23) Singapore; NAP Nanyang Technological University

      Conference

      GLSVLSI '24
      Sponsor:
      GLSVLSI '24: Great Lakes Symposium on VLSI 2024
      June 12 - 14, 2024
      FL, Clearwater, USA

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      Overall Acceptance Rate 312 of 1,156 submissions, 27%

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