CacheFormer: User-Centric Cache Prediction for Personal Vehicles using AutoFormer Network

A neural architecture for user-centric vehicular caching is introduced, termed as CacheFormer. It leverages autoformer for cache content prediction that positions vehicles as autonomous micro-servers that operate independently within personal area networks. The architecture employs multi-horizon prediction with five-minute slot granularity. It enables lookahead through decomposition of application patterns into trend and seasonal components. Integration of sparse auto-correlation mechanisms yields in reduced complexity while maintaining prediction accuracy. Further, an age-aware eviction mechanism combines forecasted demand with cache staleness metrics, which eliminates separate prediction and eviction modules. Experimental evaluation on 3,463 application sessions demonstrates convergence within 50 epochs in 27.5 minutes on consumer hardware, at high hit rates post-convergence across all popularity quartiles. Moreover, bimodal correlation distribution reveals successful temporal pattern extraction for 75% of applications. Results validate that adopting autoformer improves cache efficiency while preserving data sovereignty and eliminating dependencies on external cloud infrastructure during user mobility.