FusionNet Lite: Compact Federated Deep Learning for Predictive Maintenance under Label Heterogeneity and Temporal Generalization
Year: 2026
Type: preprint
Abstract: Federated learning (FL) supports collaborative predictive maintenance (PdM), but compactness alone does not establish reliable failure detection or consistent explanations. We evaluate FusionNet~Lite, a 1,420-parameter feature-axis classifier, against four lightweight baselines under centralized training and Federated Averaging (FedAvg). Ten matched core seeds and validation-selected checkpoints are used with stratified AI4I~2020 partitions and a fixed-row chronological MetroPT3 holdout. Separate studies extend the three-client core to ten clients and examine architectural, optimization and participation sensitivities. On MetroPT3, FedAvg FusionNet~Lite achieves area under the receiver operating characteristic curve (AUROC) $0.9351\pm0.0462$ and area under the precision--recall curve (AUPRC) $0.2197\pm0.1971$, expressed as mean and sample standard deviation. Switching from original-grid validation-F$_1$ to expanded-grid validation-F$_2$ selection changes mean test F$_2$ from $0.0423\pm0.0995$ to $0.1566\pm0.2044$, retaining substantial variability. FusionNet~Lite has the smallest parameter count and lowest fixed-batch parameter--latency index among the five core architectures, while predictive rankings depend on dataset and configuration. Paired, class-specific Integrated Gradients (IG) comparisons across all five models show variable central--federated agreement. These findings support a conditional performance--complexity trade-off within a controlled benchmark. Forecasting lead time, physical energy savings and industrial deployment readiness remain unestablished; future work requires independent equipment, alternative aggregation and device-level validation.
Source: Research Square
Institution: Swinburne University of Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225631473
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