# Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Models

arXiv: https://arxiv.org/abs/2405.09903 · HTML: https://mlsanity.com/paper/2405.09903/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 66 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Threats to Federated Learning: A Survey](https://mlsanity.com/paper/2003.02133/) (2020)
- [Federated Unsupervised Representation Learning](https://mlsanity.com/paper/2010.08982/) (2020)
- [Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges](https://mlsanity.com/paper/2108.00974/) (2021)
- [VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs](https://mlsanity.com/paper/1804.06701/) (2018)
- [Federated Learning without Full Labels: A Survey](https://mlsanity.com/paper/2303.14453/) (2023)
- [Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges](https://mlsanity.com/paper/1709.06599/) (2017)
- [Federated Variational Learning for Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2108.08404/) (2021)
- [A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks](https://mlsanity.com/paper/2201.10500/) (2022)
- [A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles](https://mlsanity.com/paper/2203.05192/) (2022)
- [Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems](https://mlsanity.com/paper/1610.06810/) (2016)
- [FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2402.10082/) (2024)
- [Restricted Boltzmann machine to determine the input weights for extreme learning machines](https://mlsanity.com/paper/1708.05376/) (2017)

## Built on by (most cited first, 2 of 2)

- [FL-PBM: Pre-Training Backdoor Mitigation for Federated Learning](https://mlsanity.com/paper/2603.28673/) (2026)
- [PAMPOS: Causal Transformer-based Trajectory Prediction for Attack-Agnostic Misbehavior Detection in V2X Networks](https://mlsanity.com/paper/2605.06833/) (2026)

## Abstract (arXiv)

Federated Learning (FL) has become an attractive approach to collaboratively train Machine Learning (ML) models while data sources' privacy is still preserved. However, most of existing FL approaches are based on supervised techniques, which could require resource-intensive activities and human intervention to obtain labelled datasets. Furthermore, in the scope of cyberattack detection, such techniques are not able to identify previously unknown threats. In this direction, this work proposes a novel unsupervised FL approach for the identification of potential misbehavior in vehicular environments. We leverage the computing capabilities of public cloud services for model aggregation purposes, and also as a central repository of misbehavior events, enabling cross-vehicle learning and collective defense strategies. Our solution integrates the use of Gaussian Mixture Models (GMM) and Variational Autoencoders (VAE) on the VeReMi dataset in a federated environment, where each vehicle is intended to train only with its own data. Furthermore, we use Restricted Boltzmann Machines (RBM) for pre-training purposes, and Fedplus as aggregation function to enhance model's convergence. Our approach provides better performance (more than 80 percent) compared to recent proposals, which are usually based on supervised techniques and artificial divisions of the VeReMi dataset.