mlsanityData to 7 Oct 2026

Hierarchical Over-the-Air Federated Edge Learning

arXiv 2112.11167 ↗First posted 21 Dec 2021cs.LG cs.DCcited by 11

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) over wireless communication channels, specifically, over-the-air (OTA) model aggregation framework is considered. In OTA wireless setups, the adverse channel effects can be alleviated by increasing the number of receive antennas at the parameter server (PS), which performs model aggregation. However, the performance of OTA FL is limited by the presence of mobile users (MUs) located far away from the PS. In this paper, to mitigate this limitation, we propose hierarchical over-the-air federated learning (HOTAFL), which utilizes intermediary servers (IS) to form clusters near MUs. We provide a convergence analysis for the proposed setup, and demonstrate through theoretical and experimental results that local aggregation in each cluster before global aggregation leads to a better performance and faster convergence than OTA FL.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 21 Dec 2021.

Builds on

15 of 23 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  4. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  5. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  6. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  7. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  8. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  9. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  10. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  11. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 72
  12. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
3 more
  1. 2020Blind Federated Edge Learningcited by 42
  2. 2020Convergence of Federated Learning over a Noisy Downlinkcited by 29
  3. 2021Federated Learning with Downlink Device Selectioncited by 3

Built on by

Cited by year

Citing papers per year: 2022: 5; 2023: 2; 2024: 4.

11 papers.

  1. 2022A Survey on Over-the-Air Computationcited by 19
  2. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  3. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  4. 2022Over-the-Air Federated Edge Learning with Hierarchical Clusteringcited by 10
  5. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  6. 2024Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneitycited by 6
  7. 2022Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learningcited by 2
  8. 2023Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networkscited by 2
  9. 2022Hierarchical Over-the-Air FedGradNormcited by 1
  10. 2024Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacycited by 1
  11. 2024A Hierarchical Federated Learning Approach for the Internet of Thingscited by 0