mlsanityData to 14 Oct 2026

Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning

arXiv 2109.02353 ↗First posted 6 Sep 2021cs.IT cs.LG cs.NIcited by 2

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Abstract (arXiv)

Federated edge learning (FEEL) has emerged as a revolutionary paradigm to develop AI services at the edge of 6G wireless networks as it supports collaborative model training at a massive number of mobile devices. However, model communication over wireless channels, especially in uplink model uploading of FEEL, has been widely recognized as a bottleneck that critically limits the efficiency of FEEL. Although over-the-air computation can alleviate the excessive cost of radio resources in FEEL model uploading, practical implementations of over-the-air FEEL still suffer from several challenges, including strong straggler issues, large communication overheads, and potential privacy leakage. In this article, we study these challenges in over-the-air FEEL and leverage reconfigurable intelligent surface (RIS), a key enabler of future wireless systems, to address these challenges. We study the state-of-the-art solutions on RIS-empowered FEEL and explore the promising research opportunities for adopting RIS to enhance FEEL performance.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 6 Sep 2021.

Builds on

14 of 28 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  3. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  4. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 63
  5. 2020Wireless Federated Learning with Local Differential Privacycited by 52
  6. 2020Blind Federated Edge Learningcited by 43
  7. 2020Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learningcited by 39
  8. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  9. 2020Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approachcited by 33
  10. 2020Intelligent Reflecting Surface Aided Wireless Communications: A Tutorialcited by 26
  11. 2020Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surfacecited by 21
  12. 2020Federated Learning in Multi-RIS Aided Systemscited by 7
2 more
  1. 2020Reconfigurable-Intelligent-Surface Empowered Wireless Communications: Challenges and Opportunitiescited by 4
  2. 2019Passive Beamforming and Information Transfer Design for Reconfigurable Intelligent Surfaces Aided Multiuser MIMO Systemscited by 2

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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
  2. 2023Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channelscited by 5