联邦AirNet：用于联邦学习的数模混合神经网络传输
Federated AirNet: Hybrid Digital-Analog Neural Network Transmission for Federated Learning
摘要：
基于无线信道的联邦学习的一个关键问题是如何通过时变信道交换大量的模型参数。通常使用基于数字和模拟方案的两种类型的解决方案。基于数字的解决方案采用量化和熵编码进行压缩，而经由无线信道的传输可能由于熵编码中的全有或全无行为而导致灾难性错误。AirNet和AirComp等基于模拟的解决方案使用模拟调制进行参数传输。然而，由于源信号的大功率而没有压缩增益，这种模拟方案经常导致显著的失真。针对联邦学习中的模型参数传输问题，提出了一种新的数模混合传输方式--联邦空中网。Federated AirNet集成了低速率数字编码和节能模拟调制。数字编码提供了模型参数的基线，压缩了源信号功率。此外，从原始和编码的模型参数中获得的残差参数被模拟调制以根据瞬时无线信道质量来增强基线。我们表明，拟议的联邦AirNet产生更好的图像分类的准确性相比，基于数字和模拟的解决方案，在广泛的无线信道信噪比（SNR）。
英文摘要：
A key issue in federated learning over wireless channels is how to exchange a large number of the model parameters via time-varying channels. Two types of solutions based on digital and analog schemes are used typically. The digital-based solution takes quantization and entropy coding for compression, whereas transmissions via wireless channels may cause catastrophic errors owing to the all-or-nothing behavior in entropy coding. The analog-based solutions such as AirNet and AirComp use analog modulation for the parameter transmissions. However, such an analog scheme often causes significant distortion due to the source signal's large power without compression gain. This paper proposes a novel hybrid digital-analog transmission-Federated AirNet--for the model parameter transmissions in federated learning. The Federated AirNet integrates low-rate digital coding and energy-compact analog modulation. The digital coding offers the baseline of the model parameters and compacts the source signal power. In addition, the residual parameters, which are obtained from the original and encoded model parameters, are analog-modulated to enhance the baseline according to the instantaneous wireless channel quality. We show that the proposed Federated AirNet yields better image classification accuracy compared with the digital-based and analog-based solutions over a wide range of wireless channel signal-to-noise ratios (SNRs).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
