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Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM

  • Xun Li
  • , Qiong Wu*
  • , Pingyi Fan
  • , Kezhi Wang
  • , Nan Cheng
  • , Khaled B. Letaief
  • *Corresponding author for this work
  • Jiangnan University
  • Nanchang University
  • Tsinghua University
  • Brunel University London
  • State Key Laboratory of Integrated Services Networks
  • Hong Kong University of Science and Technology

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)293-297
Number of pages5
JournalIEEE Networking Letters
Volume7
Issue number4
DOIs
StatePublished - 2025
Externally publishedYes

Keywords

  • Federated learning
  • denoising diffusion probabilistic model
  • edge caching

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