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http://bura.brunel.ac.uk/handle/2438/32128
Title: | Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM |
Authors: | Li, X Wu, Q Fan, P Wang, K Cheng, N Letaief, KB |
Keywords: | federated learning;denoising diffusion probabilistic model;edge caching |
Issue Date: | 18-Aug-2025 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Li, X. et al. (2025) 'Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM', IEEE Networking Letters, 0 (early access), pp. 1 - 5. doi: 10.1109/LNET.2025.3599196. |
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. |
URI: | https://bura.brunel.ac.uk/handle/2438/32128 |
DOI: | https://doi.org/10.1109/LNET.2025.3599196 |
Other Identifiers: | ORCiD: Qiong Wu https://orcid.org/0000-0002-4899-1718 ORCiD: Pingyi Fan https://orcid.org/0000-0002-0658-6079 ORCiD: Kezhi Wang https://orcid.org/0000-0001-8602-0800 ORCiD: Nan Cheng https://orcid.org/0000-0001-7907-2071 ORCiD: Khaled B. Letaief https://orcid.org/0000-0003-2519-6401 |
Appears in Collections: | Dept of Computer Science Research Papers |
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