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

Hao Chen; Zavareh Bozorgasl

eess.SPFeb 17, 2026

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

Authors: Hao Chen, Zavareh Bozorgasl

Organizations: Department of Electrical and Computer Engineering, Boise State University, Boise, ID 83712 USA.

Abstract

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.

Figures & tables

Scheme	Pilot Cost	REs/round	Var.	PAPR	CSI Need
Coherent OTA–FD	High	$K+T_{\text{pilot}}$	$O(1/M)$	$>10$ dB	Yes
SCENE	None	$KS$	$O(1/(SM))$	$\approx 1$	No
Ratio–SCENE	None	$K+1$	$O(1/(SM))$	$\approx 1$	No

TABLE II: Overhead and Crossover

Fig. 1: S versus server/global test accuracy (percent) at SNR = 5 dB.

Fig. 2: S versus server/global test accuracy (percent) at SNR = 10 dB.

Fig. 3: different combinations of (S,M)=(1,16),(16,1),(2,8),(8,2),(4,4) for 100 clients, one-shot, average of 20 trials at SNR = 5 dB.

Federated Learning Aggregation

Federated learning aggregation combines client updates into shared or personalized models while balancing data heterogeneity, robustness, privacy, and communication.

See all 260 papers

Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning
May 8, 2026Hao Chen, Zavareh BozorgaslFederated Learning Aggregation Wireless Communications
Signal, Communication, and Learning Lab (SCALE Lab), Department of Electrical and Computer Engineering, Boise State University, Boise, ID 83712 USA
Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
Sep 26, 2026Ming Xiang, Nicolò Michelusi, Yonina C. Eldar +1Over-the-Air Federated Learning Federated Learning Aggregation
Northeastern University, Boston, MA, USA · Arizona State University, Tempe, AZ, USA · Weizmann Institute of Science, Rehovot, Israel
Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling
Sep 8, 2026Haifeng Wen, Nicolò Michelusi, Osvaldo Simeone +2Federated Learning Aggregation Communication-Efficient Distributed Training
IoT Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China · School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281 USA · Institute for Intelligent Networked Systems, Northeastern University London, London, E1 8PH, UK +2

May 8, 2026J/K move · Enter open · S save

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

Hao Chen, Zavareh Bozorgasl

Signal, Communication, and Learning Lab (SCALE Lab), Department of Electrical and Computer Engineering, Boise State University, Boise, ID 83712 USA

Federated Learning Aggregation Wireless Communications Over-the-Air Federated Learning Federated Learning

Open paper AskPDF 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.

Abstract

Federated Learning Aggregation

Explore similar work

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

Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks

Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling