Continual Learning at the Edge: An Agnostic IIoT Architecture
Not on the map. Why?
Abstract (arXiv)
The exponential growth of Internet-connected devices has presented challenges to traditional centralized computing systems due to latency and bandwidth limitations. Edge computing has evolved to address these difficulties by bringing computations closer to the data source. Additionally, traditional machine learning algorithms are not suitable for edge-computing systems, where data usually arrives in a dynamic and continual way. However, incremental learning offers a good solution for these settings. We introduce a new approach that applies the incremental learning philosophy within an edge-computing scenario for the industrial sector with a specific purpose: real time quality control in a manufacturing system. Applying continual learning we reduce the impact of catastrophic forgetting and provide an efficient and effective solution.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 31 references that are in the map. First posted on arXiv 16 Dec 2025.
Builds on
10 of 31 references.
References · 10
- 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
- 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
- 2017Measuring Catastrophic Forgetting in Neural Networkscited by 360
- 2017CORe50: a New Dataset and Benchmark for Continuous Object Recognitioncited by 289
- 2021A Survey of Human-in-the-loop for Machine Learningcited by 215
- 2021Essentials for Class Incremental Learningcited by 57
- 2022SparCL: Sparse Continual Learning on the Edgecited by 55
- 2021Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applicationscited by 17
- 2021Continual Learning on the Edge with TensorFlow Litecited by 9
- 2024Overcoming Catastrophic Forgetting in Tabular Data Classification: A Pseudorehearsal-based approachcited by 5
Built on by
Not cited yet.