mlsanityData to 14 Oct 2026

NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection

arXiv 2411.13000 ↗First posted 20 Nov 2024cs.IT cs.LG eess.SPcited by 4

Not on the map. Why?

Abstract (arXiv)

Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal alignment without relying on expensive channel estimation and feedback. This paper proposes NCAirFL, a CSI-free AirFL scheme based on unbiased non-coherent detection at the edge server. By exploiting binary dithering and a long-term memory based error-compensation mechanism, NCAirFL achieves a convergence rate of order O(1/√T) in terms of the average square norm of the gradient for general non-convex and smooth objectives, where T is the number of communication rounds. Experiments demonstrate the competitive performance of NCAirFL compared to vanilla FL with ideal communications and to coherent transmission-based benchmarks.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 15 of its 19 references that are in the map. First posted on arXiv 20 Nov 2024.

Builds on

15 of 19 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Sparsified SGD with Memorycited by 376
  3. 2018Federated Learning via Over-the-Air Computationcited by 219
  4. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 193
  5. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  6. 2019Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computationscited by 181
  7. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 62
  8. 2020Blind Federated Edge Learningcited by 43
  9. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  10. 2020Fast Convergence Algorithm for Analog Federated Learningcited by 17
  11. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 14
  12. 2022Non-Coherent Over-the-Air Decentralized Gradient Descentcited by 9
3 more
  1. 2021STAR-RIS Integrated Non-Orthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimizationcited by 8
  2. 2022Knowledge-aided Federated Learning for Energy-limited Wireless Networkscited by 8
  3. 2023AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memorycited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregationcited by 1
  2. 2026SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillationcited by 0
  3. 2026Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learningcited by 0
  4. 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Schedulingcited by 0