FLASH-and-Prune:Federated Learning for Automated Selection of High-band mmWave Sectors using Model Pruning

Fast sector-steering in the mmWave band for vehicular mobility scenarios is a challenge because standard-defined exhaustive search over predefined antenna sectors cannot be assuredly completed within short contact times. This paper proposes machine learning to speed up sector selection using data from multiple non-RF sensors, such as LiDAR, GPS, and camera images in the mmWave radios with large codebooks. The contributions in this paper are threefold: First, we propose a multimodal deep learning architecture that fuses the inputs from these data sources and locally predicts the sectors for best alignment at a vehicle. Second, we propose FLASH-and-Prune, which combines the knowledge from multiple vehicles by aggregating the local model parameters and exploits model pruning to optimize the model parameter exchange overhead. Third, we present a pruning strategy that takes into account the distributed nature of federated learning to adaptively prune or retrieve model weights. We validate the proposed architecture on a real-world multimodal dataset collected from an autonomous car. We observe that FLASH-and-Prune incurs 29.25% and 35.89% less overhead in the uplink and downlink, respectively, compared to standard federated learning.