Federated Learning for Anomaly Detection in Maritime Movement Data
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917124736286720 |
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| author | Graser, Anita Weißenfeld, Axel Heistracher, Clemens Dragaschnig, Melitta Widhalm, Peter |
| author_facet | Graser, Anita Weißenfeld, Axel Heistracher, Clemens Dragaschnig, Melitta Widhalm, Peter |
| contents | This paper introduces M3fed, a novel solution for federated learning of movement anomaly detection models. This innovation has the potential to improve data privacy and reduce communication costs in machine learning for movement anomaly detection. We present the novel federated learning (FL) strategies employed to train M3fed, perform an example experiment with maritime AIS data, and evaluate the results with respect to communication costs and FL model quality by comparing classic centralized M3 and the new federated M3fed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04635 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Federated Learning for Anomaly Detection in Maritime Movement Data Graser, Anita Weißenfeld, Axel Heistracher, Clemens Dragaschnig, Melitta Widhalm, Peter Machine Learning This paper introduces M3fed, a novel solution for federated learning of movement anomaly detection models. This innovation has the potential to improve data privacy and reduce communication costs in machine learning for movement anomaly detection. We present the novel federated learning (FL) strategies employed to train M3fed, perform an example experiment with maritime AIS data, and evaluate the results with respect to communication costs and FL model quality by comparing classic centralized M3 and the new federated M3fed. |
| title | Federated Learning for Anomaly Detection in Maritime Movement Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.04635 |