• Computer Science > Machine Learning [Submitted on 22 Feb 2026] Title:FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment View PDF HTML (experimental)Abstract:Bridge periodic inspection records contain sensitive information about public infrastructure, making cross-organizational data sharing impractical under existing data governance constraints. • We propose a federated framework for estimating a Continuous-Time Markov Chain (CTMC) hazard model of bridge deterioration, enabling municipalities to collaboratively train a shared benchmark model without transferring raw inspection records. • Each User holds local inspection data and trains a log-linear hazard model over three deterioration-direction transitions – Good$\to$Minor, Good$\to$Severe, and Minor$\to$Severe – with covariates for bridge age, coastline distance, and deck area. • Local optimization is performed via mini-batch stochastic gradient descent on the CTMC log-likelihood, and only a 12-dimensional pseudo-gradient vector is uploaded to a central server per communication round. • The server aggregates User updates using sample-weighted Federated Averaging (FedAvg) with momentum and gradient clipping. • All experiments in this paper are conducted on fully synthetic data generated from a known ground-truth parameter set with region-specific heterogeneity, enabling controlled evaluation of federated convergence behaviour.

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  • Computer Science > Machine Learning [Submitted on 22 Feb 2026] Title:FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment View PDF HTML (experimental)Abstract:Bridge periodic inspection records contain sensitive information about public infrastructure, making cross-organizational data sharing impractical under existing data governance constraints. We propose a federated framework for estimating a Continuous-Time Markov Chain (CTMC) hazard model of bridge deterioration, enabling municipalities to collaboratively train a shared benchmark model without transferring raw ins

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