无线联邦学习新方法，自适应加权提升精度。
Over-the-Air Federated Learning via Weighted Aggregation
- 通信时动态调整聚合权重，无需发送端信道信息。
- 在设备异构和信道干扰下，准确率比有信道信息方案高15%。
- 适合边缘计算中资源不均、信道波动的场景应用。
本文提出一种基于空中计算的新型联邦学习方案，创新性地在聚合阶段引入自适应权重，克服了传统方案依赖发送端信道状态信息（CSIT）的局限。该方法在计算异构与一般损失函数条件下，提供了收敛性边界推导的数学框架，并给出优化权重的设计准则。为此，提出了聚合代价度量及高效算法以求解最优权重。数值实验表明，即使在信道条件恶劣和设备异构的挑战下，本方案仍较使用CSIT的方案提升15%准确率，较无CSIT方案提升30%，验证了其有效性。
原文摘要 · Abstract (English)
This paper introduces a new federated learning scheme that leverages over-the-air computation. A novel feature of this scheme is the proposal to employ adaptive weights during aggregation, a facet treated as predefined in other over-the-air schemes. This can mitigate the impact of wireless channel conditions on learning performance, without needing channel state information at transmitter side (CSIT). We provide a mathematical methodology to derive the convergence bound for the proposed scheme in the context of computational heterogeneity and general loss functions, supplemented with design insights. Accordingly, we propose aggregation cost metrics and efficient algorithms to find optimized weights for the aggregation. Finally, through numerical experiments, we validate the effectiveness of the proposed scheme. Even with the challenges posed by channel conditions and device heterogeneity, the proposed scheme surpasses other over-the-air strategies by an accuracy improvement of 15% over the scheme using CSIT and 30% compared to the one without CSIT.
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