Please use this identifier to cite or link to this item: 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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