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Blind Federated Edge Learning

arXiv 2010.10030 ↗First posted 19 Oct 2020cs.IT cs.LG eess.SPcited by 42

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Abstract (arXiv)

We study federated edge learning (FEEL), where wireless edge devices, each with its own dataset, learn a global model collaboratively with the help of a wireless access point acting as the parameter server (PS). At each iteration, wireless devices perform local updates using their local data and the most recent global model received from the PS, and send their local updates to the PS over a wireless fading multiple access channel (MAC). The PS then updates the global model according to the signal received over the wireless MAC, and shares it with the devices. Motivated by the additive nature of the wireless MAC, we propose an analog `over-the-air' aggregation scheme, in which the devices transmit their local updates in an uncoded fashion. Unlike recent literature on over-the-air edge learning, here we assume that the devices do not have channel state information (CSI), while the PS has imperfect CSI. Instead, the PS is equipped multiple antennas to alleviate the destructive effect of the channel, exacerbated due to the lack of perfect CSI. We design a receive beamforming scheme at the PS, and show that it can compensate for the lack of perfect CSI when the PS has a sufficient number of antennas. We also derive the convergence rate of the proposed algorithm highlighting the impact of the lack of perfect CSI, as well as the number of PS antennas. Both the experimental results and the convergence analysis illustrate the performance improvement of the proposed algorithm with the number of PS antennas, where the wireless fading MAC becomes deterministic despite the lack of perfect CSI when the PS has a sufficiently large number of antennas.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2020.

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Citing papers per year: 2021: 9; 2022: 8; 2023: 7; 2024: 8; 2025: 6; 2026: 4.

42 papers.

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  1. 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 166
  2. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  3. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
  4. 2021Federated Edge Learning with Misaligned Over-The-Air Computationcited by 28
  5. 2022A Survey on Over-the-Air Computationcited by 19
  6. 2021Hierarchical Over-the-Air Federated Edge Learningcited by 11
  7. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  8. 2021Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learningcited by 10
  9. 2022Over-the-Air Federated Edge Learning with Hierarchical Clusteringcited by 10
  10. 2022Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learningcited by 9
  11. 2022Non-Coherent Over-the-Air Decentralized Gradient Descentcited by 9
  12. 2022Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learningcited by 7
30 more
  1. 2023Blind Federated Learning via Over-the-Air q-QAMcited by 7
  2. 2024Federated Learning via Lattice Joint Source-Channel Codingcited by 6
  3. 2024Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approachcited by 5
  4. 2024Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Aircited by 5
  5. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
  6. 2021Denoising Noisy Neural Networks: A Bayesian Approach with Compensationcited by 4
  7. 2023AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memorycited by 4
  8. 2024Private Collaborative Edge Inference via Over-the-Air Computationcited by 4
  9. 2024NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detectioncited by 4
  10. 2021CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surfacecited by 3
  11. 2021Federated Learning with Downlink Device Selectioncited by 3
  12. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processingcited by 3
  13. 2021Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learningcited by 2
  14. 2022Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMOcited by 2
  15. 2023Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networkscited by 2
  16. 2024Biased Over-the-Air Federated Learning under Wireless Heterogeneitycited by 2
  17. 2025Update Estimation and Scheduling for Over-the-Air Federated Learning with Energy Harvesting Devicescited by 2
  18. 2023Digital Over-the-Air Federated Learning in Multi-Antenna Systemscited by 1
  19. 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequencescited by 1
  20. 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guaranteescited by 1
  21. 2025Rethinking Federated Learning Over the Air: The Blessing of Scaling Upcited by 1
  22. 2026Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregationcited by 1
  23. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 0
  24. 2023Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradientcited by 0
  25. 2023Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Frameworkcited by 0
  26. 2025Over-the-Air Multi-Sensor Inference with Neural Networks Using Memristor-Based Analog Computingcited by 0
  27. 2025MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environmentscited by 0
  28. 2026Out-of-Air Computation: Enabling Structured Function Extraction from Wireless Superpositioncited by 0
  29. 2026Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalizationcited by 0
  30. 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Schedulingcited by 0