# Federated Learning for Anomaly Detection in Maritime Movement Data

arXiv: https://arxiv.org/abs/2512.04635 · HTML: https://mlsanity.com/paper/2512.04635/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 4 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications](https://mlsanity.com/paper/2304.11101/) (2023)
- [Federated Learning on Edge Sensing Devices: A Review](https://mlsanity.com/paper/2311.01201/) (2023)

## Abstract (arXiv)

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.