Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM
On the map: Diffusion models → Diffusion foundations and sampling
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 39 of its 82 references that are in the map. First posted on arXiv 5 Jun 2025.
Builds on
39 of 82 references.
Closest ancestors · 2
Same direction, newest first
- 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
- 2020Denoising Diffusion Probabilistic Models19 Jun
Other references · 37
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- 2023Enhanced C-V2X Mode 4 to Optimize Age of Information and Reliability for IoVcited by 3
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- 2016Pricing and Resource Allocation via Game Theory for a Small-Cell Video Caching Systemcited by 2
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