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

arXiv: https://arxiv.org/abs/2401.01442 · HTML: https://mlsanity.com/paper/2401.01442/

## 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 (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning](https://mlsanity.com/paper/2002.11343/) (2020)
- [Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design](https://mlsanity.com/paper/2103.14272/) (2021)
- [Convergence of Federated Learning over a Noisy Downlink](https://mlsanity.com/paper/2008.11141/) (2020)
- [Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks](https://mlsanity.com/paper/2206.02398/) (2022)
- [Scalable Hierarchical Over-the-Air Federated Learning](https://mlsanity.com/paper/2211.16162/) (2022)
- [FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning](https://mlsanity.com/paper/2108.06869/) (2021)
- [Hierarchical Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2112.11167/) (2021)
- [3GPP-inspired HetNet Model using Poisson Cluster Process: Sum-product Functionals and Downlink Coverage](https://mlsanity.com/paper/1705.01699/) (2017)

## Built on by (most cited first, 6 of 6)

- [Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air](https://mlsanity.com/paper/2409.06343/) (2024)
- [Over-the-Air Federated Learning via Weighted Aggregation](https://mlsanity.com/paper/2409.07822/) (2024)
- [Hierarchical Federated ADMM](https://mlsanity.com/paper/2409.18796/) (2024)
- [A Generalized Hierarchical Federated Learning Framework with Theoretical Guarantees](https://mlsanity.com/paper/2505.08145/) (2025)
- [A Hierarchical Federated Learning Approach for the Internet of Things](https://mlsanity.com/paper/2403.01540/) (2024)
- [Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learning](https://mlsanity.com/paper/2602.02355/) (2026)

## 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.