mlsanityData to 7 Oct 2026

Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation

arXiv 2302.06730 ↗First posted 13 Feb 2023cs.IT cs.LG cs.SYcited by 5

Not on the map. Why?

Abstract (arXiv)

Wireless federated learning (WFL) undergoes a communication bottleneck in uplink, limiting the number of users that can upload their local models in each global aggregation round. This paper presents a new multi-carrier non-orthogonal multiple-access (MC-NOMA)-empowered WFL system under an adaptive learning setting of Flexible Aggregation. Since a WFL round accommodates both local model training and uploading for each user, the use of Flexible Aggregation allows the users to train different numbers of iterations per round, adapting to their channel conditions and computing resources. The key idea is to use MC-NOMA to concurrently upload the local models of the users, thereby extending the local model training times of the users and increasing participating users. A new metric, namely, Weighted Global Proportion of Trained Mini-batches (WGPTM), is analytically established to measure the convergence of the new system. Another important aspect is that we maximize the WGPTM to harness the convergence of the new system by jointly optimizing the transmit powers and subchannel bandwidths. This nonconvex problem is converted equivalently to a tractable convex problem and solved efficiently using variable substitution and Cauchy's inequality. As corroborated experimentally using a convolutional neural network and an 18-layer residential network, the proposed MC-NOMA WFL can efficiently reduce communication delay, increase local model training times, and accelerate the convergence by over 40%, compared to its existing alternative.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 46 references that are in the map. First posted on arXiv 13 Feb 2023.

Builds on

21 of 46 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2021Convex optimizationcited by 2,479
  4. 2020Adaptive Federated Optimizationcited by 1,036
  5. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  6. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  7. 2019Asynchronous Federated Optimizationcited by 264
  8. 2018Federated Learning via Over-the-Air Computationcited by 215
  9. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  10. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  11. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  12. 2020Towards Flexible Device Participation in Federated Learningcited by 64
9 more
  1. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  2. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  3. 2021Optimal Rate Adaption in Federated Learning with Compressed Communicationscited by 15
  4. 2019Edge-Computing-Enabled Smart Cities: A Comprehensive Surveycited by 13
  5. 2019On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collectioncited by 11
  6. 2021Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systemscited by 10
  7. 2022Time-triggered Federated Learning over Wireless Networkscited by 10
  8. 2022Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learningcited by 7
  9. 2020Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MECcited by 6

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 2.

5 papers.

  1. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  2. 2023Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Surveycited by 17
  3. 2024FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networkscited by 1
  4. 2025Straggler-Resilient Federated Learning over A Hybrid Conventional and Pinching Antenna Networkcited by 1
  5. 2025Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learningcited by 0