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

• URL: http://arxiv.org/abs/2605.07263v1
• Date: Fri, 08 May 2026 05:29:14 GMT
• Title: Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning
• Abstract summary: Over-the-air federated learning (OTA-FL) reduces uplink latency by exploiting waveform superposition.<n>Conventional analog aggregation schemes typically require channel state information (CSI), channel inversion, and coherent phase alignment.
• Score: 5.14549984289357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over-the-air federated learning (OTA-FL) reduces uplink latency by exploiting waveform superposition, but conventional analog aggregation schemes typically require instantaneous channel state information (CSI), channel inversion, and coherent phase alignment, which can be difficult to maintain in practical wireless systems. This paper proposes resource-element energy difference (REED), a noncoherent aggregation primitive for continuous signed updates that avoids instantaneous CSI. REED maps the positive and negative parts of each real-valued update to transmit energies on two orthogonal resource elements with independent phase dithers, and the server estimates the signed aggregate from their energy difference. With only slow-timescale calibration of average channel powers, REED is unbiased for the desired signed sum and admits an exact closed-form variance under Rayleigh fading. We incorporate REED into full-participation FedAvg and prove a smooth nonconvex stationarity bound. Under an average per-client energy budget, the aggregation gain can be scheduled so that the REED-induced perturbation scales quadratically with the local stepsize, yielding the canonical (1/sqrt(T)) stationarity rate. Experiments on MNIST and Fashion-MNIST demonstrate that REED closely matches clean FedAvg and coherent CSIT aggregation in IID settings, while maintaining stable convergence with a moderate performance degradation under strong data heterogeneity.

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