cs.LGEmpirical
Collaborative System Failure Prognostics via Federated Longitudinal-Survival Modeling
Jul 28, 2026
This paper proposes a federated longitudinal-survival modeling framework for collaborative system failure prognostics, enabling decentralized estimation of interval-specific failure hazards, reliability curves, and system remaining useful life.
First federated longitudinal-survival modeling framework for collaborative system failure prognostics
Before reading this…
Applications
To understand this paper, make sure you know these concepts first:
Time-to-event modeling provides a systematic framework for estimating time-dependent failure risk, reliability, and remaining useful life (RUL) from longitudinal condition monitoring data. However, applying these models to distributed prognostics remains challenging because sensor trajectories and failure-time records are often stored across organizations or operational sites and cannot be centrally pooled due to privacy or proprietary constraints. Moreover, the classical Cox proportional hazards model relies on a nonseparable partial likelihood involving global risk sets, making direct optimization difficult under standard federated learning protocols. This paper presents a federated longitudinal-survival modeling framework for collaborative system failure prognostics. The proposed framework combines longitudinal sensor representation learning with a client-separable discrete-time hazard objective, enabling multiple clients to collaboratively train a prognostic model without sharing raw sensor measurements or individual failure records. Time-dependent representations extracted from multivariate sensor histories are used to estimate interval-specific failure hazards, reliability curves, and system RUL. Experiments on the four C-MAPSS turbofan engine degradation subsets under simulated decentralized settings demonstrate that the proposed framework consistently improves prognostic performance over isolated local training while maintaining performance comparable to centralized training across heterogeneous operating conditions and failure modes. These results demonstrate the potential of federated longitudinal-survival modeling for collaborative, data-aware condition monitoring and system failure prognostics.
SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework
This paper proposes SynPre-FL, a framework that combines high-fidelity synthetic…
Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inferen…
This paper proposes a decentralized collaborative learning paradigm for Tsetlin…
Test-Time Training for Modality Order Consistency in Vision-Language Models
This paper identifies modality-order sensitivity as a failure in vision-language…
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data…
This paper introduces an open-source framework for evaluating the efficacy of AI…
Adaptive Data Admission and Retention for Streaming Federated Learning
This paper proposes a framework for federated learning with limited client memor…
Transformer-based Diffusion models for Hydrological Time Series Probabilistic Imputation and Forecas…
This paper investigates the application of transformer-based diffusion models fo…
Robust Unsupervised Network Intrusion Detection via Federated Learning with Selective Aggregation un…
This paper proposes a robust training methodology for anomaly detection in netwo…
Draining the Energy Commons: Self-Defeating Over-Appropriation as a Coordination Failure in Agentic…
This paper studies coordination failures in a renewable energy commons among LLM…