mlsanityData to 7 Oct 2026

Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints

arXiv 2106.00490 ↗First posted 31 May 2021cs.LG cs.IT math.ITcited by 34

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Abstract (arXiv)

Machine learning and wireless communication technologies are jointly facilitating an intelligent edge, where federated edge learning (FEEL) is a promising training framework. As wireless devices involved in FEEL are resource limited in terms of communication bandwidth, computing power and battery capacity, it is important to carefully schedule them to optimize the training performance. In this work, we consider an over-the-air FEEL system with analog gradient aggregation, and propose an energy-aware dynamic device scheduling algorithm to optimize the training performance under energy constraints of devices, where both communication energy for gradient aggregation and computation energy for local training are included. The consideration of computation energy makes dynamic scheduling challenging, as devices are scheduled before local training, but the communication energy for over-the-air aggregation depends on the l2-norm of local gradient, which is known after local training. We thus incorporate estimation methods into scheduling to predict the gradient norm. Taking the estimation error into account, we characterize the performance gap between the proposed algorithm and its offline counterpart. Experimental results show that, under a highly unbalanced local data distribution, the proposed algorithm can increase the accuracy by 4.9% on CIFAR-10 dataset compared with the myopic benchmark, while satisfying the energy constraints.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 40 of its 49 references that are in the map. First posted on arXiv 31 May 2021.

Builds on

40 of 49 references.

References · 40

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2019Towards Federated Learning at Scale: System Designcited by 826
  6. 2015Train faster, generalize better: Stability of stochastic gradient descentcited by 547
  7. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  8. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  9. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  10. 2017Gradient Sparsification for Communication-Efficient Distributed Optimizationcited by 199
  11. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  12. 2019Federated Learning over Wireless Fading Channelscited by 178
28 more
  1. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  2. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  3. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  4. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  5. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  6. 2018LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learningcited by 95
  7. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 94
  8. 2018Wireless Network Intelligence at the Edgecited by 90
  9. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  10. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  11. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 72
  12. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  13. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  14. 2020Blind Federated Edge Learningcited by 42
  15. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  16. 2019Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Datacited by 38
  17. 2019Machine Learning in the Aircited by 32
  18. 2019High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learningcited by 32
  19. 2020Energy-Efficient Federated Edge Learning with Joint Communication and Computation Designcited by 30
  20. 2020Convergence of Federated Learning over a Noisy Downlinkcited by 29
  21. 2021Federated Edge Learning with Misaligned Over-The-Air Computationcited by 28
  22. 2020Communicate to Learn at the Edgecited by 27
  23. 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 25
  24. 2019Energy-Aware Analog Aggregation for Federated Learning with Redundant Datacited by 23
  25. 2020Edge Learning with Timeliness Constraints: Challenges and Solutionscited by 10
  26. 2017EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networkscited by 5
  27. 2020Gradient Statistics Aware Power Control for Over-the-Air Federated Learningcited by 5
  28. 2020Over-the-Air Computing for Wireless Data Aggregation in Massive IoTcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 10; 2024: 8; 2025: 7; 2026: 3.

34 papers.

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  1. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  2. 2024Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigmcited by 13
  3. 2022Task-Oriented Over-the-Air Computation for Multi-Device Edge AIcited by 12
  4. 2022Over-the-Air Federated Edge Learning with Hierarchical Clusteringcited by 10
  5. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  6. 2023Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learningcited by 9
  7. 2022Efficient Wireless Federated Learning with Partial Model Aggregationcited by 8
  8. 2022MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehiclescited by 8
  9. 2022Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learningcited by 7
  10. 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
  11. 2024Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approachcited by 6
  12. 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selectioncited by 4
22 more
  1. 2024Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Methodcited by 4
  2. 2024Federated Learning With Energy Harvesting Devices: An MDP Frameworkcited by 4
  3. 2023Dynamic Scheduling for Federated Edge Learning with Streaming Datacited by 3
  4. 2024Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learningcited by 3
  5. 2025Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learningcited by 3
  6. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  7. 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequencescited by 1
  8. 2024Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Designcited by 1
  9. 2024A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learningcited by 1
  10. 2025FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learningcited by 1
  11. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 0
  12. 2023Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learningcited by 0
  13. 2023Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradientcited by 0
  14. 2023Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learningcited by 0
  15. 2024Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraintcited by 0
  16. 2025Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approachcited by 0
  17. 2025Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsificationcited by 0
  18. 2025Learned Digital Over-the-Air Computing for Federated Edge Learningcited by 0
  19. 2025Learned Digital Codes for Over-the-Air Computation in Federated Edge Learningcited by 0
  20. 2026Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networkscited by 0
  21. 2026Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learningcited by 0
  22. 2026Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalizationcited by 0