无需信道估计的无线联邦学习新方法，提升通信效率。
NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection
- 基于非相干检测与二值抖动实现无信道状态信息传输
- 收敛速度达 $\mathcal{O}(1/\sqrt{T})$，在非凸平滑目标下有效
- 适合低开销、高鲁棒性边缘联邦学习场景
无线联邦学习（AirFL）利用多址信道原生计算能力。长期以来，其核心挑战在于无需昂贵信道估计与反馈即可实现相干信号对齐。本文提出NCAirFL，一种基于边缘服务器无偏非相干检测的无信道状态信息（CSI-free）AirFL方案。通过引入二值抖动与基于长期记忆的误差补偿机制，NCAirFL在一般非凸光滑目标下实现了平均梯度平方范数的收敛率 $\mathcal{O}(1/\sqrt{T})$，其中 $T$ 为通信轮数。实验表明，该方案性能优于理想通信下的标准联邦学习及基于相干传输的基准方法。
原文摘要 · Abstract (English)
Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal alignment without relying on expensive channel estimation and feedback. This paper proposes NCAirFL, a CSI-free AirFL scheme based on unbiased non-coherent detection at the edge server. By exploiting binary dithering and a long-term memory based error-compensation mechanism, NCAirFL achieves a convergence rate of order $\mathcal{O}(1/\sqrt{T})$ in terms of the average square norm of the gradient for general non-convex and smooth objectives, where $T$ is the number of communication rounds. Experiments demonstrate the competitive performance of NCAirFL compared to vanilla FL with ideal communications and to coherent transmission-based benchmarks.
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