# Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC

arXiv: https://arxiv.org/abs/2006.13044 · HTML: https://mlsanity.com/paper/2006.13044/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

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

- [Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles](https://mlsanity.com/paper/2210.15496/) (2022)
- [Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks](https://mlsanity.com/paper/2304.08996/) (2023)
- [Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation](https://mlsanity.com/paper/2302.06730/) (2023)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks](https://mlsanity.com/paper/2403.03157/) (2024)
- [Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks](https://mlsanity.com/paper/2303.10254/) (2023)

## Abstract (arXiv)

Federated learning (FL) is a highly pursued machine learning technique that can train a model centrally while keeping data distributed. Distributed computation makes FL attractive for bandwidth limited applications especially in wireless communications. There can be a large number of distributed edge devices connected to a central parameter server (PS) and iteratively download/upload data from/to the PS. Due to the limited bandwidth, only a subset of connected devices can be scheduled in each round. There are usually millions of parameters in the state-of-art machine learning models such as deep learning, resulting in a high computation complexity as well as a high communication burden on collecting/distributing data for training. To improve communication efficiency and make the training model converge faster, we propose a new scheduling policy and power allocation scheme using non-orthogonal multiple access (NOMA) settings to maximize the weighted sum data rate under practical constraints during the entire learning process. NOMA allows multiple users to transmit on the same channel simultaneously. The user scheduling problem is transformed into a maximum-weight independent set problem that can be solved using graph theory. Simulation results show that the proposed scheduling and power allocation scheme can help achieve a higher FL testing accuracy in NOMA based wireless networks than other existing schemes.