FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection
FedTR combines federated learning and transfer learning to address data scarcity and complexity in industrial visual inspection, achieving high accuracy on label defect identification.
-->
[Submitted on 9 Jul 2026]
Title:FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection
View a PDF of the paper titled FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection, by Vikash Sathiamoorthy and 9 other authors
View PDF HTML (experimental)
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.
Comments: Author's accepted version. Published in Proceedings of the Great Lakes Symposium on VLSI 2024 (GLSVLSI '24)
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.08014 [cs.CV]
(or arXiv:2607.08014v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.08014
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Proceedings of the Great Lakes Symposium on VLSI 2024 (GLSVLSI '24), pp. 310-314, 2024
Related DOI:
https://doi.org/10.1145/3649476.3658768
DOI(s) linking to related resources
Submission history
From: Weichen Liu [view email] [v1] Thu, 9 Jul 2026 00:40:33 UTC (599 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection, by Vikash Sathiamoorthy and 9 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-07
Change to browse by:
cs cs.LG
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)