A Label Efficient Semi Self-Supervised Learning Framework for IoT Devices in Industrial Process
Abstract
The industrial sector has experienced a tremendous advancement in deep supervised learning due to its representation ability, but it comes with high computing and labeled data demands. Recently, the demand for intelligent IoT devices on assembly and disassembly lines has surged. This necessitates algorithms that can use data to make intelligent decisions and a framework that can enable multiple IoT devices to learn collaboratively. Further, huge image generation using IoTs also needs an efficient data annotation scheme for classification problems. WeCollab is one such framework in federated learning that significantly reduces human efforts in data annotation with breakthroughs in self-supervised learning. The proposed framework is generic and can be adapted to any specific image data generated by industrial robots involved in assembly and disassembly lines. Our method outperforms supervised learning by 25% and 20% on the CIFAR-10 and CINIC-10 datasets, respectively, for the labeling task. We generate pseudo labels for the unlabeled part of the data and train a model to achieve 30% better test accuracy on CIFAR-10 and 20% better test accuracy on the CINIC-10 dataset as compared to supervised learning. Extensive experiments unveil the effectiveness of the method and proposed combination of loss functions used by WeCollab.
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NLU is fundamental in applications like sentiment analysis, language translation, information extraction, and more.
“NLU is fundamental in applications like sentiment analysis, language translation, information extraction, and more [194], [195], [196].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [196]
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“…have focused on developing sophisticated loss functions and regularization techniques, aimed at enhancing the performance and generalization capabilities of supervised learning models, ensuring they remain robust and effective across a wide range of tasks and data types [166], [167], [168].”
Background
- Authors
- Bharti, V., Kumar, A., Purohit, V., Singh, R., Singh, A. K., & Singh, S. K.
- Affiliations
- Indian Institute of Technology BHU (IN)
- National Institute of Technology Delhi (IN)
- National Institute of Technology Patna (IN)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2024-02
- Volume
- 20
- Issue
- 2
- Pages
- 2253-2262
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4382119832
- Cited by
- 15
- References
- 22
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 3 · 2023: 1
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- semi-supervised learning, self-supervised learning, label efficiency, federated learning, IoT devices, industrial robots, pseudo-labeling, cooperative learning, image classification, loss function combination
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