Federated Distillation Assisted Vehicle Edge Caching Scheme Based on Lightweight DDPM
This work addresses privacy and efficiency issues in vehicle edge caching for vehicle users, representing an incremental improvement over traditional federated learning methods.
The paper tackles the challenge of accurately predicting content for vehicle edge caching while protecting user privacy and reducing communication overhead, achieving significant reductions in communication overhead and improvements in cache hit percentage.
Vehicle edge caching is a promising technology that can significantly reduce the latency for vehicle users (VUs) to access content by pre-caching user-interested content at edge nodes. It is crucial to accurately predict the content that VUs are interested in without exposing their privacy. Traditional federated learning (FL) can protect user privacy by sharing models rather than raw data. However, the training of FL requires frequent model transmission, which can result in significant communication overhead. Additionally, vehicles may leave the road side unit (RSU) coverage area before training is completed, leading to training failures. To address these issues, in this letter, we propose a federated distillation-assisted vehicle edge caching scheme based on lightweight denoising diffusion probabilistic model (LDPM). The simulation results demonstrate that the proposed vehicle edge caching scheme has good robustness to variations in vehicle speed, significantly reducing communication overhead and improving cache hit percentage.