Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling
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, leveragin...
| Authors | Haifeng Wen, Nicolò Michelusi, Osvaldo Simeone et al. |
|---|---|
| Primary category | cs.IT |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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