# Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments

arXiv: https://arxiv.org/abs/2405.09276 · HTML: https://mlsanity.com/paper/2405.09276/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 15 May 2024.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [The Non-IID Data Quagmire of Decentralized Machine Learning](https://mlsanity.com/paper/1910.00189/) (2019)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Fast-Convergent Federated Learning with Adaptive Weighting](https://mlsanity.com/paper/2012.00661/) (2020)
- [Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach](https://mlsanity.com/paper/2011.10282/) (2020)
- [Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity](https://mlsanity.com/paper/2005.12326/) (2020)
- [Accurate and Fast Federated Learning via IID and Communication-Aware Grouping](https://mlsanity.com/paper/2012.04857/) (2020)
- [Federated Learning with Erroneous Communication Links](https://mlsanity.com/paper/2201.12991/) (2022)
- [E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI](https://mlsanity.com/paper/2008.01553/) (2020)
- [Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks](https://mlsanity.com/paper/2302.10747/) (2023)
- [Over-The-Air Clustered Wireless Federated Learning](https://mlsanity.com/paper/2211.03363/) (2022)

## Built on by (most cited first, 1 of 1)

- [Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments](https://mlsanity.com/paper/2505.06268/) (2025)

## Abstract (arXiv)

Non-independent and identically distributed (Non- IID) data adversely affects federated learning (FL) while heterogeneity in communication quality can undermine the reliability of model parameter transmission, potentially degrading wireless FL convergence. This paper proposes a novel dual-segment clustering (DSC) strategy that jointly addresses communication and data heterogeneity in FL. This is achieved by defining a new signal-to-noise ratio (SNR) matrix and information quantity matrix to capture the communication and data heterogeneity, respectively. The celebrated affinity propagation algorithm is leveraged to iteratively refine the clustering of clients based on the newly defined matrices effectively enhancing model aggregation in heterogeneous environments. The convergence analysis and experimental results show that the DSC strategy can improve the convergence rate of wireless FL and demonstrate superior accuracy in heterogeneous environments compared to classical clustering methods.