Federated learning (FL) enables privacy-preserving predictive maintenance across distributed aerospace fleets, but gradient communication overhead constrains deployment on bandwidth-limited IoT nodes. This paper investigates the impact of symmetric uniform quantization ($b \in \{32,8,4,2\}$ bits) on the accuracy--efficiency trade-off of a custom-designed lightweight 1-D convolutional model (AeroConv1D, 9\,697 parameters) trained via FL on the NASA C-MAPSS benchmark under a realistic Non-IID client partition. Using a rigorous multi-seed evaluation ($N=10$ seeds), we show that INT4 achieves accuracy \emph{statistically indistinguishable} from FP32 on both FD001 ($p=0.341$) and FD002 ($p=0.264$ MAE, $p=0.534$ NASA score) while delivering an $8\times$ reduction in gradient communication cost (37.88~KiB $\to$ 4.73~KiB per round). A key methodological finding is that naïve IID client partitioning artificially suppresses variance; correct Non-IID evaluation reveals the true operational instability of extreme quantization, demonstrated via a direct empirical IID vs.\ Non-IID comparison. INT2 is empirically characterized as unsuitable: while it achieves lower MAE on FD002 through extreme quantization-induced over-regularization, this apparent gain is accompanied by catastrophic NASA score instability (CV\,=\,45.8\% vs.\ 22.3\% for FP32), confirming non-reproducibility under heterogeneous operating conditions. Analytical FPGA resource projections on the Xilinx ZCU102 confirm that INT4 fits within hardware constraints (85.5\% DSP utilization), potentially enabling a complete FL pipeline on a single SoC. The full simulation codebase and FPGA estimation scripts are publicly available at https://github.com/therealdeadbeef/aerospace-fl-quantization.

翻译：联邦学习(FL)能够在分布式航空机队中实现隐私保护的预测性维护，但梯度通信开销限制了其在带宽受限的物联网节点上的部署。本文研究了对称均匀量化（$b \in \{32,8,4,2\}$比特）对自定义轻量级一维卷积模型（AeroConv1D，9\,697个参数）精度-效率权衡的影响，该模型通过FL在NASA C-MAPSS基准数据集上训练，并采用符合实际的非独立同分布（Non-IID）客户端划分。通过严格的多种子评估（$N=10$个种子），我们证明INT4在FD001（$p=0.341$）和FD002（MAE的$p=0.264$，NASA评分的$p=0.534$）上实现了与FP32统计上无法区分的精度，同时将梯度通信成本降低8倍（每轮37.88~KiB $\to$ 4.73~KiB）。一个关键的方法论发现是：朴素的独立同分布（IID）客户端划分会人为抑制方差；正确的Non-IID评估揭示了极端量化的真实运行不稳定性，这通过直接的IID与非IID实证对比得以证明。INT2被经验性判定为不适用：虽然它在FD002上通过极端量化诱导的过度正则化实现了更低的MAE，但这种表面增益伴随着灾难性的NASA评分不稳定性（变异系数CV=45.8\%对比FP32的22.3\%），证实了在异构运行条件下不可复现。在Xilinx ZCU102上的分析性FPGA资源预估表明，INT4符合硬件约束（85.5\% DSP利用率），有可能在单个片上系统上实现完整的FL流水线。完整的仿真代码库和FPGA估算脚本已公开发布于https://github.com/therealdeadbeef/aerospace-fl-quantization。