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Life sciences · Preprint
arXiv · September 8, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint proposes a non-coherent over-the-air federated learning (NCAirFL) protocol designed to reduce physical layer requirements (CSI, synchronization, calibration) in wireless federated learning. Theoretical analysis claims convergence at O(1/√T) rate matching FedAvg, and simulation experiments on MNIST and CIFAR-10 show performance close to ideal communication-based FedAvg. The work is early-stage, unreviewed, and lacks real deployment or comparison to existing non-coherent systems.
Preprint: theoretical protocol design with convergence analysis and simulation-based validation. Simulated federated edge learning devices over a broadband single-antenna multiple-access channel; no human subjects or real network deployment.. Intervention: Non-coherent over-the-air federated learning (NCAirFL) protocol with proposed device scheduling policy. Compared with: Communication-ideal FedAvg (standard federated averaging).
Convergence bound established for NCAirFL achieving O(1/√T) rate, same order as communication-ideal FedAvg Experimental results on MNIST and CIFAR-10 show learning performance close to FedAvg in practical settings Proposed device scheduling policy substantially accelerates convergence under data and wireless resource heterogeneity
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A theoretical protocol with convergence analysis and experimental validation on standard datasets, but without clinical or real-world deployment evidence, peer review, or comparison to deployed federated learning systems.
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To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) exploits waveform superposition over multiple-access channels (MACs) for analog model aggregation. However, coherent AirFL typically relies on stringent PHY-layer conditions such as accurate channel state information (CSI), tight time/frequency synchronization, and frequent transceiver calibration for signal alignment. However, these requirements, if not impossible to be met, incur substantial communication and computation overhead. In this paper, we propose a non-coherent AirFL (NCAirFL) protocol over a broadband single-antenna MAC, leveraging binary dithering, unbiased non-coherent detection, and long-term error feedback to waive the need for instantaneous CSI. For NCAirFL with general smooth non-convex objectives and a constant learning rate, we establish a convergence bound achieving the convergence rate in the same order of $\mathcal{O}(1/\sqrt{T})$ as communication-ideal FedAvg, where $T$ is the total number of communication rounds. To further improve communication efficiency under data and wireless resource heterogeneity, we also derive a lower bound on the expected single-round objective decrease in the global loss conditioned on device scheduling, building upon which a surrogate objective function is obtained for jointly optimal device selection and power control. Experimental results on MNIST and CIFAR-10 corroborate that NCAirFL achieves learning performance close to FedAvg in practical settings, with the proposed device scheduling policy substantially accelerating convergence.
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