mlsanityData to 7 Oct 2026

Optimized Power Control Design for Over-the-Air Federated Edge Learning

arXiv 2106.09316 ↗First posted 17 Jun 2021cs.IT eess.SP math.ITcited by 45

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Abstract (arXiv)

This paper investigates the transmission power control in over-the-air federated edge learning (Air-FEEL) system. Different from conventional power control designs (e.g., to minimize the individual mean squared error (MSE) of the over-the-air aggregation at each round), we consider a new power control design aiming at directly maximizing the convergence speed. Towards this end, we first analyze the convergence behavior of Air-FEEL (in terms of the optimality gap) subject to aggregation errors at different communication rounds. It is revealed that if the aggregation estimates are unbiased, then the training algorithm would converge exactly to the optimal point with mild conditions; while if they are biased, then the algorithm would converge with an error floor determined by the accumulated estimate bias over communication rounds. Next, building upon the convergence results, we optimize the power control to directly minimize the derived optimality gaps under both biased and unbiased aggregations, subject to a set of average and maximum power constraints at individual edge devices. We transform both problems into convex forms, and obtain their structured optimal solutions, both appearing in a form of regularized channel inversion, by using the Lagrangian duality method. Finally, numerical results show that the proposed power control policies achieve significantly faster convergence for Air-FEEL, as compared with benchmark policies with fixed power transmission or conventional MSE minimization.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jun 2021.

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Citing papers per year: 2021: 1; 2022: 7; 2023: 11; 2024: 13; 2025: 7; 2026: 6.

45 papers.

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  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  2. 2022Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learningcited by 59
  3. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  4. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  5. 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
  6. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  7. 2022Over-the-Air Federated Learning via Second-Order Optimizationcited by 11
  8. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  9. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  10. 2024Federated Learning via Lattice Joint Source-Channel Codingcited by 6
  11. 2024Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Aircited by 5
  12. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
33 more
  1. 2025Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learningcited by 5
  2. 2022CFLIT: Coexisting Federated Learning and Information Transfercited by 4
  3. 2023Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysiscited by 4
  4. 2023AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memorycited by 4
  5. 2024Adaptive Federated Learning Over the Aircited by 4
  6. 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
  7. 2024Federated Learning With Energy Harvesting Devices: An MDP Frameworkcited by 4
  8. 2025Biased Federated Learning under Wireless Heterogeneitycited by 4
  9. 2024Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offscited by 3
  10. 2024Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocationcited by 3
  11. 2023Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clippingcited by 2
  12. 2022Matching Pursuit Based Scheduling for Over-the-Air Federated Learningcited by 1
  13. 2022Adaptive Top-K in SGD for Communication-Efficient Distributed Learningcited by 1
  14. 2023Over-the-Air Federated Averaging with Limited Power and Privacy Budgetscited by 1
  15. 2023Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioningcited by 1
  16. 2024Optimal Batch Allocation for Wireless Federated Learningcited by 1
  17. 2024Task-oriented Over-the-air Computation for Edge-device Co-inference with Balanced Classification Accuracycited by 1
  18. 2025Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecturecited by 1
  19. 2025Rethinking Federated Learning Over the Air: The Blessing of Scaling Upcited by 1
  20. 2025Differential Privacy as a Perk: Federated Learning over Multiple-Access Fading Channels with a Multi-Antenna Base Stationcited by 1
  21. 2025Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-offcited by 1
  22. 2023Over-the-Air Computation in OFDM Systems with Imperfect Channel State Informationcited by 0
  23. 2023Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learningcited by 0
  24. 2023Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradientcited by 0
  25. 2024Waveform Design for Over-the-Air Computingcited by 0
  26. 2024Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysiscited by 0
  27. 2025Timely Parameter Updating in Over-the-Air Federated Learningcited by 0
  28. 2026Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learningcited by 0
  29. 2026Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learningcited by 0
  30. 2026AirMoE: Statistic-Augmented Over-the-Air MoE for Collaborative Intelligencecited by 0
  31. 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Schedulingcited by 0
  32. 2026AirGC-CD: Gaussian-Circulant Precoding for Exactly Debiasable PAPR Reduction in Over-the-Air Federated Learningcited by 0
  33. 2026Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networkscited by 0