mlsanityData to 14 Oct 2026

Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks

arXiv 1910.14648 ↗First posted 31 Oct 2019cs.IT eess.SP math.ITcited by 39

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Abstract (arXiv)

Federated learning (FL) is a machine learning model that preserves data privacy in the training process. Specifically, FL brings the model directly to the user equipments (UEs) for local training, where an edge server periodically collects the trained parameters to produce an improved model and sends it back to the UEs. However, since communication usually occurs through a limited spectrum, only a portion of the UEs can update their parameters upon each global aggregation. As such, new scheduling algorithms have to be engineered to facilitate the full implementation of FL. In this paper, based on a metric termed the age of update (AoU), we propose a scheduling policy by jointly accounting for the staleness of the received parameters and the instantaneous channel qualities to improve the running efficiency of FL. The proposed algorithm has low complexity and its effectiveness is demonstrated by Monte Carol simulations.

Key facts. Cited by 39 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Oct 2019.

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Citing papers per year: 2021: 8; 2022: 9; 2023: 10; 2024: 7; 2025: 3; 2026: 2.

39 papers.

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  1. 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
  2. 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
  3. 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
  4. 2021Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Schedulingcited by 9
  5. 2023Delay Sensitive Hierarchical Federated Learning with Stochastic Local Updatescited by 8
  6. 2023Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approachcited by 8
  7. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  8. 2023Federated Learning While Providing Model as a Service: Joint Training and Inference Optimizationcited by 8
  9. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  10. 2021Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learningcited by 7
  11. 2022A Contribution-based Device Selection Scheme in Federated Learningcited by 7
  12. 2021Data-Aware Device Scheduling for Federated Edge Learningcited by 6
27 more
  1. 2022Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relayingcited by 5
  2. 2024Version age-based client scheduling policy for federated learningcited by 5
  3. 2021Collaborative Learning over Wireless Networks: An Introductory Overviewcited by 4
  4. 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selectioncited by 4
  5. 2021Federated Learning with Downlink Device Selectioncited by 3
  6. 2023Value of Information and Timing-aware Scheduling for Federated Learningcited by 3
  7. 2021Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learningcited by 2
  8. 2021Federated learning and next generation wireless communications: A survey on bidirectional relationshipcited by 2
  9. 2022Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMOcited by 2
  10. 2023Asynchronous Online Federated Learning with Reduced Communication Requirementscited by 2
  11. 2021Nothing Wasted: Full Contribution Enforcement in Federated Edge Learningcited by 1
  12. 2022Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learningcited by 1
  13. 2023Ensemble DNN for Age-of-Information Minimization in UAV-assisted Networkscited by 1
  14. 2024Load Balancing in Federated Learningcited by 1
  15. 2024rAge-k: Communication-Efficient Federated Learning Using Age Factorcited by 1
  16. 2026Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregationcited by 1
  17. 2022Federated Stochastic Gradient Descent Begets Self-Induced Momentumcited by 0
  18. 2022Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updatescited by 0
  19. 2023Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learningcited by 0
  20. 2023Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Reviewcited by 0
  21. 2024CAFe: Cost and Age aware Federated Learningcited by 0
  22. 2024Federated Learning with Multi-resolution Model Broadcastcited by 0
  23. 2024A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Datacited by 0
  24. 2025MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environmentscited by 0
  25. 2025Balancing Client Participation in Federated Learning Using AoIcited by 0
  26. 2025Timely Parameter Updating in Over-the-Air Federated Learningcited by 0
  27. 2026Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learningcited by 0