mlsanityData to 14 Oct 2026

Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneity

arXiv 2401.01442 ↗First posted 2 Jan 2024cs.IT cs.AI cs.LGcited by 6

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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 learning method designed to address these challenges, along with a scalable transmission scheme that efficiently uses a single wireless resource through over-the-air computation. To provide resistance against data heterogeneity, we employ gradient aggregations. Meanwhile, the impact of interference is minimized through optimized receiver normalizing factors. For this, we model a multi-cluster wireless network using stochastic geometry, and characterize the mean squared error of the aggregation estimations as a function of the network parameters. We show that despite the interference and the data heterogeneity, the proposed scheme achieves high learning accuracy and can significantly outperform the conventional hierarchical algorithm.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 10 of its 17 references that are in the map. First posted on arXiv 2 Jan 2024.

Builds on

10 of 17 references.

References · 10

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  3. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  4. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  5. 2020Convergence of Federated Learning over a Noisy Downlinkcited by 29
  6. 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
  7. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  8. 2021FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learningcited by 11
  9. 2021Hierarchical Over-the-Air Federated Edge Learningcited by 11
  10. 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 14 Oct)

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

6 papers.

  1. 2024Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Aircited by 5
  2. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
  3. 2024Hierarchical Federated ADMMcited by 4
  4. 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guaranteescited by 2
  5. 2024A Hierarchical Federated Learning Approach for the Internet of Thingscited by 0
  6. 2026Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learningcited by 0