mlsanityData to 7 Oct 2026

SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillation

arXiv 2602.15326 ↗First posted 17 Feb 2026eess.SP cs.AI cs.DCcited by 0

Not on the map. Why?

Abstract (arXiv)

We propose SCENE (Self-Centering Noncoherent Estimator), a pilot-free and phase-invariant aggregation primitive for over-the-air federated distillation (OTA-FD). Each device maps its soft-label (class-probability) vector to nonnegative transmit energies under constant per-round power and constant-envelope signaling (PAPR near 1). At the server, a self-centering energy estimator removes the noise-energy offset and yields an unbiased estimate of the weighted soft-label average, with variance decaying on the order of 1/(SM) in the number of receive antennas M and repetition factor S. We also develop a pilot-free ratio-normalized variant that cancels unknown large-scale gains, provide a convergence bound consistent with coherent OTA-FD analyses, and present an overhead-based crossover comparison. SCENE targets short-coherence and hardware-constrained regimes, where avoiding per-round CSI is essential: it trades a modest noncoherent variance constant for zero uplink pilots, unbiased aggregation, and hardware-friendly transmission, and can outperform coherent designs when pilot overhead is non-negligible.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 28 references that are in the map. First posted on arXiv 17 Feb 2026.

Builds on

20 of 28 references.

References · 20

  1. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  2. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  3. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  4. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  5. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
  6. 2020Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Datacited by 125
  7. 2022Knowledge Distillation for Federated Learning: a Practical Guidecited by 31
  8. 2020Communication-Efficient Federated Distillationcited by 28
  9. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  10. 2024Federated Distillation: A Surveycited by 14
  11. 2022Over-the-Air Federated Edge Learning with Hierarchical Clusteringcited by 10
  12. 2023Over-the-Air Federated Learning and Optimizationcited by 10
8 more
  1. 2021Over-the-Air Computation with DFT-spread OFDM for Federated Edge Learningcited by 9
  2. 2022Distributed Learning over a Wireless Network with Non-coherent Majority Vote Computationcited by 8
  3. 2023Knowledge Distillation in Federated Edge Learning: A Surveycited by 5
  4. 2022A Demonstration of Over-the-Air Computation for Federated Edge Learningcited by 4
  5. 2024NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detectioncited by 4
  6. 2025Optimal Transceiver Design in Over-the-Air Federated Distillationcited by 2
  7. 2024Peak-Controlled Logits Poisoning Attack in Federated Distillationcited by 1
  8. 2025Soft-Label Caching and Sharpening for Communication-Efficient Federated Distillationcited by 1

Built on by

Not cited yet.