mlsanityData to 14 Oct 2026

Device Scheduling with Fast Convergence for Wireless Federated Learning

arXiv 1911.00856 ↗First posted 3 Nov 2019cs.NI cs.IT cs.LGcited by 46

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Abstract (arXiv)

Owing to the increasing need for massive data analysis and model training at the network edge, as well as the rising concerns about the data privacy, a new distributed training framework called federated learning (FL) has emerged. In each iteration of FL (called round), the edge devices update local models based on their own data and contribute to the global training by uploading the model updates via wireless channels. Due to the limited spectrum resources, only a portion of the devices can be scheduled in each round. While most of the existing work on scheduling focuses on the convergence of FL w.r.t. rounds, the convergence performance under a total training time budget is not yet explored. In this paper, a joint bandwidth allocation and scheduling problem is formulated to capture the long-term convergence performance of FL, and is solved by being decoupled into two sub-problems. For the bandwidth allocation sub-problem, the derived optimal solution suggests to allocate more bandwidth to the devices with worse channel conditions or weaker computation capabilities. For the device scheduling sub-problem, by revealing the trade-off between the number of rounds required to attain a certain model accuracy and the latency per round, a greedy policy is inspired, that continuously selects the device that consumes the least time in model updating until achieving a good trade-off between the learning efficiency and latency per round. The experiments show that the proposed policy outperforms other state-of-the-art scheduling policies, with the best achievable model accuracy under training time budgets.

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Nov 2019.

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Citing papers per year: 2021: 15; 2022: 9; 2023: 8; 2024: 6; 2025: 5; 2026: 3.

46 papers.

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  1. 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
  2. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  3. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  4. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  5. 2021Efficient Device Scheduling with Multi-Job Federated Learningcited by 29
  6. 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
  7. 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
  8. 2022FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneitycited by 20
  9. 2022Multi-Job Intelligent Scheduling with Cross-Device Federated Learningcited by 20
  10. 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
  11. 2021HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environmentscited by 15
  12. 2022Task-Oriented Over-the-Air Computation for Multi-Device Edge AIcited by 14
34 more
  1. 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
  2. 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
  3. 2021Efficient Federated Meta-Learning over Multi-Access Wireless Networkscited by 13
  4. 2021Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networkscited by 10
  5. 2021CSAFL: A Clustered Semi-Asynchronous Federated Learning Frameworkcited by 10
  6. 2024Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networkscited by 9
  7. 2021Federated Learning over Noisy Channels: Convergence Analysis and Design Examplescited by 8
  8. 2023Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participationcited by 8
  9. 2021Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networkscited by 7
  10. 2022Confederated Learning: Federated Learning with Decentralized Edge Serverscited by 7
  11. 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Thingscited by 6
  12. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  13. 2022Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relayingcited by 5
  14. 2021Collaborative Learning over Wireless Networks: An Introductory Overviewcited by 4
  15. 2022Time Minimization in Hierarchical Federated Learningcited by 4
  16. 2022Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learningcited by 3
  17. 2022Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMOcited by 2
  18. 2023Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networkscited by 2
  19. 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networkscited by 2
  20. 2025Efficient Federated Learning with Timely Update Disseminationcited by 2
  21. 2023Scheduling and Communication Schemes for Decentralized Federated Learningcited by 1
  22. 2024Task-oriented Over-the-air Computation for Edge-device Co-inference with Balanced Classification Accuracycited by 1
  23. 2024A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learningcited by 1
  24. 2025Adaptive Deadline and Batch Layered Synchronized Federated Learningcited by 1
  25. 2021FedProf: Selective Federated Learning with Representation Profilingcited by 0
  26. 2023Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Reviewcited by 0
  27. 2023VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learningcited by 0
  28. 2023Knowledge Rumination for Client Utility Evaluation in Heterogeneous Federated Learningcited by 0
  29. 2024FLASH: Federated Learning Across Simultaneous Heterogeneitiescited by 0
  30. 2024Random Aggregate Beamforming for Over-the-Air Federated Learning in Large-Scale Networkscited by 0
  31. 2025FedOC: Multi-Server FL with Overlapping Client Relays in Wireless Edge Networkscited by 0
  32. 2026Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networkscited by 0
  33. 2026Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learningcited by 0
  34. 2026Client and Training Data Selection for Computationally Efficient Synchronized Federated Learningcited by 0