mlsanityData to 7 Oct 2026

Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning

arXiv 2605.07263 ↗First posted 8 May 2026eess.SP cs.AI cs.DCcited by 0

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Abstract (arXiv)

Over-the-air federated learning (OTA-FL) reduces uplink latency by aggregating client updates directly over the wireless multiple-access channel. Coherent analog aggregation realizes this idea by aligning the phases and amplitudes of simultaneously transmitted waveforms, which typically requires synchronization, instantaneous channel-state information (CSI), phase compensation, and power control. Noncoherent energy detection removes the need for phase-coherent combining, but a single energy measurement is nonnegative and, therefore, cannot represent signed model updates. This paper introduces resource-element energy difference (REED), a noncoherent physical-layer primitive for continuous signed aggregation. REED maps the positive and negative parts of each real-valued update to transmit energies on paired orthogonal resource elements and estimates the signed sum by subtracting the corresponding received energies. The construction uses slow-timescale calibration of average channel powers, but does not require instantaneous transmitter- or receiver-side CSI or channel inversion. For independent Rayleigh fading, we derive exact first- and second-moment expressions for single-shot REED and for a chip-diverse extension that spreads each coordinate over multiple independently faded paired chips. The resulting variance laws separate fading-induced self-noise, signal-noise interaction, and receiver-noise fluctuation, giving an explicit diversity-resource tradeoff. More->The rest of abstract is in the paper.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 32 references that are in the map. First posted on arXiv 8 May 2026.

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23 of 32 references.

References · 23

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  7. 2018Federated Learning via Over-the-Air Computationcited by 215
  8. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  9. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  10. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 72
  11. 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 45
  12. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
11 more
  1. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 13
  2. 2022Over-the-Air Computation for 6G: Foundations, Technologies, and Applicationscited by 12
  3. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  4. 2022Non-Coherent Over-the-Air Decentralized Gradient Descentcited by 9
  5. 2018Blind Over-the-Air Computation and Data Fusion via Provable Wirtinger Flowcited by 8
  6. 2022Distributed Learning over a Wireless Network with Non-coherent Majority Vote Computationcited by 8
  7. 2023Blind Federated Learning via Over-the-Air q-QAMcited by 7
  8. 2024Waveforms for Computing Over the Aircited by 6
  9. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
  10. 2024Adaptive Federated Learning Over the Aircited by 4
  11. 2024NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detectioncited by 4

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