通过个性化联邦蒸馏降低通信开销，提升车端缓存效率。
Personalized Federated Distillation Assisted Vehicle Edge Caching Strategy
- 结合联邦学习与知识蒸馏，实现隐私保护下的个性化内容预测。
- 相比传统方法减少显著通信开销，且在不同车速下表现稳定。
- 适合车联网中需低延迟、高隐私保护的边缘缓存场景。
车端边缘缓存技术可通过在边缘节点预缓存用户感兴趣的内容，显著降低车辆用户访问内容的延迟。准确预测车辆用户兴趣内容且不泄露隐私至关重要。传统联邦学习通过共享模型而非原始数据保护隐私，但频繁的模型传输带来显著通信开销，且车辆可能在训练完成前离开路边单元（RSU）覆盖范围，导致训练失败。为此，本文提出一种个性化联邦蒸馏辅助的车端边缘缓存策略。仿真结果表明，该策略对车速变化具有良好的鲁棒性，显著降低了通信开销。
原文摘要 · Abstract (English)
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 paper, we propose a personalized federated distillation assisted vehicle edge caching strategy. The simulation results demonstrate that the proposed vehicle edge caching strategy has good robustness to variations in vehicle speed, significantly reducing communication overhead.
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