# Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks

arXiv: https://arxiv.org/abs/2403.03157 · HTML: https://mlsanity.com/paper/2403.03157/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 53 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [SCAFFOLD: Stochastic Controlled Averaging for Federated Learning](https://mlsanity.com/paper/1910.06378/) (2019)
- [Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning](https://mlsanity.com/paper/2007.07174/) (2020)
- [Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control](https://mlsanity.com/paper/2006.05459/) (2020)
- [Federated Learning for 6G Communications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2006.02931/) (2020)
- [Federated Learning and Wireless Communications](https://mlsanity.com/paper/2005.05265/) (2020)
- [Adaptive Federated Learning With Gradient Compression in Uplink NOMA](https://mlsanity.com/paper/2003.01344/) (2020)
- [A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art](https://mlsanity.com/paper/1906.08452/) (2019)
- [FedMe: Federated Learning via Model Exchange](https://mlsanity.com/paper/2110.07868/) (2021)
- [Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach](https://mlsanity.com/paper/2209.06623/) (2022)
- [A Contribution-based Device Selection Scheme in Federated Learning](https://mlsanity.com/paper/2203.05369/) (2022)
- [Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC](https://mlsanity.com/paper/2006.13044/) (2020)
- [Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigm](https://mlsanity.com/paper/2104.12005/) (2021)
- [Mobile Reconfigurable Intelligent Surfaces for NOMA Networks: Federated Learning Approaches](https://mlsanity.com/paper/2105.09462/) (2021)

## Built on by (most cited first, 2 of 2)

- [A survey on Clustered Federated Learning: Taxonomy, Analysis and Applications](https://mlsanity.com/paper/2501.17512/) (2025)
- [Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization](https://mlsanity.com/paper/2608.01426/) (2026)

## Abstract (arXiv)

This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-orthogonal multiple access (NOMA) under non-independent and identically distributed (non-IID) datasets, where multiple devices participate in the aggregation with time limitations and a finite number of sub-channels. A detailed theoretical analysis of the generalization gap that measures the degree of non-IID in the data distribution is presented. Following that, solutions to address the challenges posed by non-IID conditions are proposed with the analysis of the properties. Specifically, users' data distributions are parameterized as concentration parameters and grouped using spectral clustering, with Dirichlet distribution serving as the prior. The investigation into the generalization gap and convergence rate guides the design of sub-channel assignments through the matching-based algorithm, and the power allocation is achieved by Karush-Kuhn-Tucker (KKT) conditions with the derived closed-form solution. The extensive simulation results show that the proposed cluster-based FL framework can outperform FL baselines in terms of both test accuracy and convergence rate. Moreover, jointly optimizing sub-channel and power allocation in NOMA-enhanced networks can lead to a significant improvement.