FedAttention: Federated Attention-Based Fusion Learning for Multi-Modal Beamforming in IoV

Advanced beamforming techniques enable stable vehicular communication and address mmWave limitations by accurately directing the signal. However, traditional beamforming techniques struggle in high-speed vehicles due to time-intensive codebook processing and image-based feedback adjustments. Multi-modal beamforming using real-time data like GPS, cameras, and LiDAR to train the Deep Learning (DL) models can provide adaptive beam steering, improving reliability in dynamic conditions. Despite this, centralized systems involving large raw data transmission are vulnerable to saturation and malicious interference, and they neglect privacy concerns, necessitating a new framework. This paper proposes a novel federated attentionbased fusion learning framework named FedAttention for multimodal beamforming in the Internet-of-Vehicle (IoV). FedAttention further improves the model generalization ability by utilizing the CNN-Transformer architecture and making full use of the Multi-access Edge Computing (MEC) servers for the potential federated split learning to enhance efficiency. Based on the realworld datasets, FedAttention achieves 98.16 % in Top-5 accuracy and 82.09 % in Top-1 accuracy, a 26.86 % improvement compared to the current FLASH framework with less wall clock time, showing its training efficiency and robustness.