Abstract
Edge caching is an emerging technology that empowers caching units at edge nodes, allowing users to fetch contents of interest that have been pre-cached at the edge nodes. The key to pre-caching is to maximize the cache hit percentage for cached content without compromising users’ privacy. In this letter, we propose a federated learning (FL) assisted edge caching scheme based on lightweight architecture denoising diffusion probabilistic model (LDPM). Our simulation results verify that our proposed scheme achieves a higher cache hit percentage compared to existing FL-based methods and baseline methods.
| Original language | English |
|---|---|
| Pages (from-to) | 293-297 |
| Number of pages | 5 |
| Journal | IEEE Networking Letters |
| Volume | 7 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2025 |
| Externally published | Yes |
Keywords
- Federated learning
- denoising diffusion probabilistic model
- edge caching
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