Quantization Impact on the Accuracy and Communication Efficiency Trade-off in Federated Learning for Aerospace Predictive Maintenance

Abdelkarim Loukili

cs.LGApr 9, 2026

Quantization Impact on the Accuracy and Communication Efficiency Trade-off in Federated Learning for Aerospace Predictive Maintenance

Authors: Abdelkarim Loukili

Abstract

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 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.

Figures & tables

Layer	Type / Configuration	Output Shape	Params
Input	Time-series window	$(B,14,50)$	0
1	Conv1D ( $k=3,s=1,p=1$ ) + ReLU	$(B,32,50)$	1,376
2	MaxPool1D ( $k=2,s=2$ )	$(B,32,25)$	0
3	Conv1D ( $k=3,s=1,p=1$ ) + ReLU	$(B,64,25)$	6,208
4	AdaptiveAvgPool1D	$(B,64,1)$	0
5	Flatten	$(B,64)$	0

Table 1: AeroConv1D architecture specification (9,697 parameters total).

B

: batch size. The parameter count is verified programmatically at simulation startup via an assertion.

	FD001		FD002
Client	Mean RUL	EMD	Mean RUL	EMD
$k=1$	66.5	8.9	72.0	3.4
$k=2$	74.1	1.2	77.6	2.2
$k=3$	68.8	6.5	78.1	2.6
$k=4$	73.3	2.0	74.8	0.6
$k=5$	86.8	11.4	71.5	4.0

Table 2: Per-client mean RUL (cycles) for seed 42, confirming Non-IID label heterogeneity. EMD: Earth Mover’s Distance from the global RUL histogram.

Sub.	Cfg	MAE (cycles)	$p_{\text{MAE}}$	Score $S$ ( $\times 10^{3}$ )	$p_{S}$	CV S
FD001	FP32	$17.52\pm 0.47$	—	$449\pm 123$	—	27.3%
	INT8	$17.51\pm 0.48$	0.520	$447\pm 127$	0.746	28.6%
	INT4	$17.48\pm 0.51$	0.341	$452\pm 115$	0.802	25.3%
	INT2	$19.03\pm 1.62$	0.018	$802\pm 573$	0.064	72.0%
FD002	FP32	$26.99\pm 1.69$	—	$923\pm 206$	—	22.3%
	INT8	$27.24\pm 1.40$	0.265	$951\pm 167$	0.364	16.9%

Table 3: Test-set results: Mean

\pm

Std over 10 random seeds.

p

-values from two-tailed paired

t

-test vs. FP32 (

n=10

\text{df}=9

). Bold :

p<0.05

. NASA score

S

reported as sum over all test windows (Eq. 3 ). CV S : coefficient of variation of

S

across seeds. Cohen’s

\hat{d}

N=10

carries 95% CI

\approx\hat{d}\pm 0.95

; interpret as directional only.

Figure 1: MAE convergence over 20 FL rounds on C-MAPSS FD001. Shaded bands:

\pm

1 std over 10 seeds. FP32, INT8, and INT4 converge to indistinguishable final MAE; INT2 exhibits slower convergence and higher variance.

Partition	Config	MAE (cycles)	Score $S$ ( $\times 10^{3}$ )
IID	FP32	$17.34\pm 0.36$	$421\pm 41$
IID	INT4	$17.28\pm 0.26$	$440\pm 31$
Non-IID	FP32	$17.52\pm 0.47$	$449\pm 123$
Non-IID	INT4	$17.48\pm 0.51$	$452\pm 115$

Table 4: Methodological bias: IID vs. Non-IID partitioning on FD001 (mean

\pm

std over 10 seeds). IID suppresses variance, making INT4 appear to outperform FP32; Non-IID reveals statistical parity.

Figure 2: MAE by subset and quantization level. Hatching indicates FD002 (6 operating conditions). Asterisk (*):

p<0.05

vs. FP32 on MAE. Warning: The lower MAE of INT2 on FD002 is an over-regularization artefact, not a genuine accuracy improvement (see Section 5.4 ).

Figure 3: NASA score

S

convergence on C-MAPSS FD001. Lower is better. INT2 early-round values reach

10^{9}

and are off-scale; the y-axis is clipped for readability. Negative values arise when systematic under-prediction dominates; INT2 oscillates between extreme positive and negative scores, illustrating non-reproducibility.

Figure 4: Gradient-distortion privacy proxy

\mathcal{L}_{\mathrm{priv}}

on FD001 (log scale) over 20 FL rounds. Higher values indicate greater gradient distortion and higher gradient-inversion attack cost [ 20 , 4 ] . FP32 is omitted (

\mathcal{L}_{\mathrm{priv}}=0

by definition).

\mathcal{L}_{\mathrm{priv}}

is not a formal DP bound; see Section 3.5 .

Figure 5: Accuracy–communication trade-off on FD001. Error bars:

\pm

1 std over 10 seeds. Arrow: FP32

\to

INT4 operating point. Annotated

p

-value from paired

t

-test on NASA score (INT4 vs. FP32).

Cfg	LUT	%LUT	DSP	%DSP	Lat.	Fit
FP32	51 717	18.9%	17 239	684.1%	16 $\mu$ s	$\times$
INT8	12 929	4.7%	4 309	171.0%	4 $\mu$ s	$\times$
INT4	6 464	2.4%	2 154	85.5%	2 $\mu$ s	$\checkmark$
INT2	3 232	1.2%	1 077	42.7%	1 $\mu$ s	$\checkmark$

Table 5: Analytical FPGA resource projections — Xilinx ZCU102 (274 080 LUT

|

2 520 DSP

|

912 BRAM36). Latency at 500 MHz, sequence length 50. Comm. cost excludes per-layer scale overhead (

8\times 4

\approx

0.03 KiB). Projections derived from hls4ml scaling model; not validated on physical silicon.

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Quantization in Federated Learning: Methods, Challenges and Future Directions

Farwa Ikram, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino

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Federated Learning (FL) has become a foundational paradigm for privacy-preserving distributed intelligence, yet its scalability remains fundamentally constrained by communication bottlenecks, device heterogeneity, and the challenges of training under statistically non-IID data. Quantization is one of the most effective mechanisms for mitigating these limitations, reducing both uplink/downlink payloads and on-device computation. This paper provides the first FL-centric systematic review of quantization, introducing a novel taxonomy organized around FL-specific dimensions, including client heterogeneity, aggregation consistency, communication-scheduling adaptation, non-IID robustness, privacy/security integration, and hardware/energy co-optimization. Beyond cataloging existing methods, we analyze how quantization interacts with core FL behaviors such as client drift, partial participation, convergence stability, secure aggregation, and differential privacy. We further identify cross-method insights, open research gaps, and design guidelines for practitioners deploying quantized FL on mobile, IoT, and edge platforms. This survey thus establishes quantization not merely as a compression technique, but as a fundamental systems component shaping the performance, robustness, and practicality of modern FL.

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