Continual Learning at the Edge: An Agnostic IIoT Architecture
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912768261619712 |
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| author | García-Santaclara, Pablo Fernández-Castro, Bruno Díaz-Redondo, Rebeca P. Calvo-Moa, Carlos Mariño-Bodelón, Henar |
| author_facet | García-Santaclara, Pablo Fernández-Castro, Bruno Díaz-Redondo, Rebeca P. Calvo-Moa, Carlos Mariño-Bodelón, Henar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Continual Learning at the Edge: An Agnostic IIoT Architecture García-Santaclara, Pablo Fernández-Castro, Bruno Díaz-Redondo, Rebeca P. Calvo-Moa, Carlos Mariño-Bodelón, Henar Machine Learning 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. |
| title | Continual Learning at the Edge: An Agnostic IIoT Architecture |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.14311 |