FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914227369803776 |
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| author | S, Devika Hari, Vishnu Narang, Pratik Alladi, Tejasvi Yu, F. Richard |
| author_facet | S, Devika Hari, Vishnu Narang, Pratik Alladi, Tejasvi Yu, F. Richard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24345 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles S, Devika Hari, Vishnu Narang, Pratik Alladi, Tejasvi Yu, F. Richard Cryptography and Security Artificial Intelligence 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. |
| title | FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2512.24345 |