基于FedAvg的CTMC危险模型用于联邦桥梁劣化评估
FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment
AI总结：
本文提出基于FedAvg的CTMC危险模型，用于联邦桥梁劣化评估，通过本地优化和联邦学习实现协同训练，保护数据隐私并提升生命周期规划的准确性。
摘要：
桥梁定期检查记录包含关于公共基础设施的敏感信息，这使得在现有数据治理限制下跨组织数据共享变得不切实际。我们提出了一种联邦框架，用于估计桥梁劣化连续时间马尔可夫链（CTMC）危险模型，使市政部门能够协同训练一个共享基准模型，而无需转移原始检查记录。每个用户持有本地检查数据，并在三个劣化方向转换上训练对数线性危险模型——Good→Minor、Good→Severe和Minor→Severe——并使用桥龄、海岸距离和桥面面积等协变量。通过在CTMC对数似然上进行小批量随机梯度下降进行本地优化，并且每个通信轮次只上传一个12维的伪梯度向量到中央服务器。服务器使用带有动量和梯度裁剪的样本加权联邦平均（FedAvg）聚合用户更新。本文中的所有实验均在完全合成的数据上进行，该数据由已知的真实参数集生成，具有区域特定的异质性，从而能够对联邦收敛行为进行受控评估。在异构用户中的模拟结果表明，平均负对数似然的收敛性一致，随着用户规模的增加，聚合梯度范数减小。此外，联邦更新机制提供了一种自然的参与激励：注册本地检查数据集到共享技术标准平台的用户将获得定期更新的全球基准参数——这些信息无法从本地数据中单独获得——从而能够进行基于证据的生命周期规划，而无需放弃数据主权。
英文摘要：
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. Simulation results across heterogeneous Users show consistent convergence of the average negative log-likelihood, with the aggregated gradient norm decreasing as User scale increases. Furthermore, the federated update mechanism provides a natural participation incentive: Users who register their local inspection datasets on a shared technical-standard platform receive in return the periodically updated global benchmark parameters -- information that cannot be obtained from local data alone -- thereby enabling evidence-based life-cycle planning without surrendering data sovereignty.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
