mlsanityData to 7 Oct 2026

Over-the-Air Federated Edge Learning with Hierarchical Clustering

arXiv 2207.09232 ↗First posted 19 Jul 2022cs.LG cs.DCcited by 10

Not on the map. Why?

Abstract (arXiv)

We examine federated learning (FL) with over-the-air (OTA) aggregation, where mobile users (MUs) aim to reach a consensus on a global model with the help of a parameter server (PS) that aggregates the local gradients. In OTA FL, MUs train their models using local data at every training round and transmit their gradients simultaneously using the same frequency band in an uncoded fashion. Based on the received signal of the superposed gradients, the PS performs a global model update. While the OTA FL has a significantly decreased communication cost, it is susceptible to adverse channel effects and noise. Employing multiple antennas at the receiver side can reduce these effects, yet the path-loss is still a limiting factor for users located far away from the PS. To ameliorate this issue, in this paper, we propose a wireless-based hierarchical FL scheme that uses intermediate servers (ISs) to form clusters at the areas where the MUs are more densely located. Our scheme utilizes OTA cluster aggregations for the communication of the MUs with their corresponding IS, and OTA global aggregations from the ISs to the PS. We present a convergence analysis for the proposed algorithm, and show through numerical evaluations of the derived analytical expressions and experimental results that utilizing ISs results in a faster convergence and a better performance than the OTA FL alone while using less transmit power. We also validate the results on the performance using different number of cluster iterations with different datasets and data distributions. We conclude that the best choice of cluster aggregations depends on the data distribution among the MUs and the clusters.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 26 of its 36 references that are in the map. First posted on arXiv 19 Jul 2022.

Builds on

26 of 36 references.

References · 26

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  6. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  7. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  8. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  9. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  10. 2019Federated Learning over Wireless Fading Channelscited by 178
  11. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  12. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
14 more
  1. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  2. 2020Byzantine-Resilient Secure Federated Learningcited by 93
  3. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  4. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 72
  5. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  6. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  7. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  8. 2020Blind Federated Edge Learningcited by 42
  9. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
  10. 2021Federated Edge Learning with Misaligned Over-The-Air Computationcited by 28
  11. 2020Communicate to Learn at the Edgecited by 27
  12. 2020Byzantine-Resilient SGD in High Dimensions on Heterogeneous Datacited by 17
  13. 2021Hierarchical Over-the-Air Federated Edge Learningcited by 11
  14. 2021Federated Learning with Downlink Device Selectioncited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 3; 2026: 4.

10 papers.

  1. 2022A Survey on Over-the-Air Computationcited by 19
  2. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  3. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processingcited by 3
  4. 2025Update Estimation and Scheduling for Over-the-Air Federated Learning with Energy Harvesting Devicescited by 2
  5. 2023Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioningcited by 1
  6. 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guaranteescited by 1
  7. 2026Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learningcited by 0
  8. 2026SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillationcited by 0
  9. 2026Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Designcited by 0
  10. 2026Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalizationcited by 0