mlsanityData to 7 Oct 2026

Scalable Hierarchical Over-the-Air Federated Learning

arXiv 2211.16162 ↗First posted 29 Nov 2022cs.IT cs.AI cs.LGcited by 14

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Abstract (arXiv)

When implementing hierarchical federated learning over wireless networks, scalability assurance and the ability to handle both interference and device data heterogeneity are crucial. This work introduces a new two-level learning method designed to address these challenges, along with a scalable over-the-air aggregation scheme for the uplink and a bandwidth-limited broadcast scheme for the downlink that efficiently use a single wireless resource. To provide resistance against data heterogeneity, we employ gradient aggregations. Meanwhile, the impact of uplink and downlink interference is minimized through optimized receiver normalizing factors. We present a comprehensive mathematical approach to derive the convergence bound for the proposed algorithm, applicable to a multi-cluster wireless network encompassing any count of collaborating clusters, and provide special cases and design remarks. As a key step to enable a tractable analysis, we develop a spatial model for the setup by modeling devices as a Poisson cluster process over the edge servers and rigorously quantify uplink and downlink error terms due to the interference. Finally, we show that despite the interference and data heterogeneity, the proposed algorithm not only achieves high learning accuracy for a variety of parameters but also significantly outperforms the conventional hierarchical learning algorithm.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 17 of its 44 references that are in the map. First posted on arXiv 29 Nov 2022.

Builds on

17 of 44 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Conditioncited by 526
  3. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  4. 2019Federated Learning over Wireless Fading Channelscited by 178
  5. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  6. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  7. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  8. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  9. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  10. 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 45
  11. 2020Convergence of Federated Learning over a Noisy Downlinkcited by 29
  12. 2020Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networkscited by 29
5 more
  1. 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
  2. 2021Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysiscited by 13
  3. 2021Hierarchical Over-the-Air Federated Edge Learningcited by 11
  4. 2021FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learningcited by 10
  5. 20173GPP-inspired HetNet Model using Poisson Cluster Process: Sum-product Functionals and Downlink Coveragecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 2; 2026: 4.

14 papers.

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  1. 2023Blind Federated Learning via Over-the-Air q-QAMcited by 7
  2. 2024Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneitycited by 6
  3. 2024Federated Learning via Lattice Joint Source-Channel Codingcited by 6
  4. 2024Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Aircited by 5
  5. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
  6. 2024Hierarchical Federated ADMMcited by 4
  7. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processingcited by 3
  8. 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guaranteescited by 1
  9. 2024A Hierarchical Federated Learning Approach for the Internet of Thingscited by 0
  10. 2024A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approachcited by 0
  11. 2026Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learningcited by 0
  12. 2026Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networkscited by 0
2 more
  1. 2026Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Designcited by 0
  2. 2026AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systemscited by 0