mlsanityData to 7 Oct 2026

Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks

arXiv 2304.08996 ↗First posted 18 Apr 2023cs.LG cs.NIcited by 8

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Abstract (arXiv)

In federated learning (FL), distributed clients can collaboratively train a shared global model while retaining their own training data locally. Nevertheless, the performance of FL is often limited by the slow convergence due to poor communications links when FL is deployed over wireless networks. Due to the scarceness of radio resources, it is crucial to select clients precisely and allocate communication resource accurately for enhancing FL performance. To address these challenges, in this paper, a joint optimization problem of client selection and resource allocation is formulated, aiming to minimize the total time consumption of each round in FL over a non-orthogonal multiple access (NOMA) enabled wireless network. Specifically, considering the staleness of the local FL models, we propose an age of update (AoU) based novel client selection scheme. Subsequently, the closed-form expressions for resource allocation are derived by monotonicity analysis and dual decomposition method. In addition, a server-side artificial neural network (ANN) is proposed to predict the FL models of clients who are not selected at each round to further improve FL performance. Finally, extensive simulation results demonstrate the superior performance of the proposed schemes over FL performance, average AoU and total time consumption.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 29 of its 56 references that are in the map. First posted on arXiv 18 Apr 2023.

Builds on

29 of 56 references.

References · 29

  1. 2021Convex optimizationcited by 2,479
  2. 2018Federated Learning with Non-IID Datacited by 1,099
  3. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  4. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  5. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  6. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  7. 2019Reliable Federated Learning for Mobile Networkscited by 92
  8. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  9. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  10. 2020Age of Information: An Introduction and Surveycited by 64
  11. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  12. 2019Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networkscited by 39
17 more
  1. 2020Federated Learning for 6G Communications: Challenges, Methods, and Future Directionscited by 32
  2. 2018Toward Understanding the Impact of Staleness in Distributed Machine Learningcited by 28
  3. 2022Towards Efficient Communications in Federated Learning: A Contemporary Surveycited by 24
  4. 2020Adaptive Federated Learning With Gradient Compression in Uplink NOMAcited by 19
  5. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  6. 2021Federated Learning over Energy Harvesting Wireless Networkscited by 17
  7. 2021Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?cited by 13
  8. 2021Efficient Federated Meta-Learning over Multi-Access Wireless Networkscited by 13
  9. 2022Over-the-Air Federated Learning via Second-Order Optimizationcited by 11
  10. 2022A Survey on Participant Selection for Federated Learning in Mobile Networkscited by 11
  11. 2022Time-triggered Federated Learning over Wireless Networkscited by 10
  12. 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
  13. 2022Service Delay Minimization for Federated Learning over Mobile Devicescited by 9
  14. 2020Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MECcited by 6
  15. 2021Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trendscited by 6
  16. 2022Energy-Aware Edge Association for Cluster-based Personalized Federated Learningcited by 6
  17. 2022Impact of NOMA on Age of Information: A Grant-Free Transmission Perspectivecited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 2.

8 papers.

  1. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
  2. 2025Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learningcited by 3
  3. 2024Load Balancing in Federated Learningcited by 1
  4. 2023Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learningcited by 0
  5. 2025Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networkscited by 0
  6. 2025Balancing Client Participation in Federated Learning Using AoIcited by 0
  7. 2026Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learningcited by 0
  8. 2026Polar Code Based Federated Learning: Convergence Analysis and Resource Allocationcited by 0