mlsanityData to 14 Oct 2026

Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning

arXiv 2305.16854 ↗First posted 26 May 2023cs.LG cs.DCcited by 9

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Abstract (arXiv)

Federated learning (FL) is a popular privacy-preserving distributed training scheme, where multiple devices collaborate to train machine learning models by uploading local model updates. To improve communication efficiency, over-the-air computation (AirComp) has been applied to FL, which leverages analog modulation to harness the superposition property of radio waves such that numerous devices can upload their model updates concurrently for aggregation. However, the uplink channel noise incurs considerable model aggregation distortion, which is critically determined by the device scheduling and compromises the learned model performance. In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named PO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation. We prove the unbiasedness of this aggregation scheme and demonstrate the convergence of PO-FL on both convex and non-convex loss functions. Our convergence bounds unveil that the device scheduling affects the learning performance through the communication distortion and global update variance. Based on the convergence analysis, we further develop a channel and gradient-importance aware algorithm to optimize the device scheduling probabilities in PO-FL. Extensive simulation results show that the proposed PO-FL framework with channel and gradient-importance awareness achieves faster convergence and produces better models than baseline methods.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 22 of its 42 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

22 of 42 references.

References · 22

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2021Convex optimizationcited by 2,515
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  5. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  6. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  7. 2018Federated Learning via Over-the-Air Computationcited by 219
  8. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  9. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 73
  10. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  11. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  12. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
10 more
  1. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 36
  2. 2022DELTA: Diverse Client Sampling for Fasting Federated Learningcited by 24
  3. 2022Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systemscited by 20
  4. 2021Joint Optimization of Communications and Federated Learning Over the Aircited by 14
  5. 2021Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneitycited by 12
  6. 2021Relay-Assisted Cooperative Federated Learningcited by 9
  7. 2022Knowledge-aided Federated Learning for Energy-limited Wireless Networkscited by 8
  8. 2021User Scheduling for Federated Learning Through Over-the-Air Computationcited by 7
  9. 2022Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Designcited by 6
  10. 2022CFLIT: Coexisting Federated Learning and Information Transfercited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 4; 2026: 1.

9 papers.

  1. 2023Green Edge AI: A Contemporary Surveycited by 25
  2. 2024Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clientscited by 7
  3. 2024Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Designcited by 3
  4. 2025Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learningcited by 3
  5. 2025pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Datacited by 3
  6. 2024How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learningcited by 2
  7. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  8. 2025Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecturecited by 1
  9. 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Schedulingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach
  2. 2026Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
  3. 2022Over-the-Air Federated Learning via Second-Order Optimization
  4. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
  5. 2025Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-off
  6. 2022Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control
  7. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions
  8. 2024Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Framework