FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles
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Abstract (arXiv)
This works presents an encoder-only transformer built with minimum layers for intrusion detection in the domain of Connected and Autonomous Vehicles using Federated Learning.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 30 Dec 2025.
Builds on
9 of 32 references.
References · 9
- 2017Attention Is All You Needcited by 59,932
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2018Subsampled R'enyi Differential Privacy and Analytical Moments Accountantcited by 234
- 2022Unlocking High-Accuracy Differentially Private Image Classification through Scalecited by 218
- 2019R'enyi Differential Privacy of the Sampled Gaussian Mechanismcited by 199
- 2018VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETscited by 14
- 2024A Robust Multi-Stage Intrusion Detection System for In-Vehicle Network Security using Hierarchical Federated Learningcited by 4
- 2024MistralBSM: Leveraging Mistral-7B for Vehicular Networks Misbehavior Detectioncited by 3
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