FedSecureFormer: Lightweight Intrusion Detection in CAVs

This paper addresses a critical security concern in Connected and Autonomous Vehicles (CAVs) by proposing a federated learning approach for intrusion detection. The use of a lightweight transformer architecture is particularly relevant given the resource constraints of CAVs. The focus on federated learning is also important for privacy and scalability in a distributed environment.

• •Proposes a federated learning framework for intrusion detection in CAVs.
• •Employs a lightweight, encoder-only transformer architecture.
• •Aims to address security concerns while considering resource constraints and privacy.