Degradation

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16 papers in the last four weeks, up 33% on the four weeks before. 0.2% of all new papers.

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Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics
Apr 21, 2026Yuhan Hu, Xiaolei FangFederated Learning Predictive Maintenance
Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA
LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
Apr 21, 2026Xiang Chen, Hao Li, Jiangxin Dong +54Image Restoration Degradation
Predicting LLM Compression Degradation from Spectral Statistics
Apr 20, 2026Mingxue XuLarge Language Model Compression Low-Rank Structure
Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
Hero-Mamba: Mamba-based Dual Domain Learning for Underwater Image Enhancement
Apr 17, 2026Tejeswar Pokuri, Shivarth RaiUnderwater Image Enhancement Methods Mamba-Based Models
Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal - 576104, India
Continuous ageing trajectory representations for knee-aware lifetime prediction of lithium-ion batteries across heterogeneous dataset
Apr 17, 2026Agnieszka Pregowska, Stefan MarynowiczLithium-Ion Batteries Degradation
Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs
Apr 17, 2026Sai Srinivas Kancheti, Aditya Sanjiv Kanade, Vineeth N. Balasubramanian +1Spatial Reasoning Stable Spatial Understanding
IIT, Hyderabad · Microsoft Research India
Consent Chain Degradation in Embodied Multi-Agent Systems: Bridging the Gap Between AI Agent Governance and Robot Ethics
Apr 17, 2026Mehmet HaklidirModel-Based Multi-Agent Systems Artificial Intelligence Governance
Artificial Intelligence Institute, TÜBİTAK BİLGEM, Kocaeli, Turkey
EcoFair: Energy-Efficient Inference Routing for Edge AI under Data Degradation
Mar 27, 2026Mostafa Anoosha, Dhavalkumar Thakker, Kuniko Paxton +4Artificial Intelligence Lifecycle Artificial Intelligence Systems
School of Digital and Physical Science, University of Hull Kingston upon Hull, HU6 7RX, U.K.
Semantic Misalignment in Vision-Language Models under Perceptual Degradation
Jan 13, 2026Guo Cheng, Huang LiRecent Vision-Language Models Autonomous Driving Perception
Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics
Jan 7, 2026Ibai Ramirez, Jokin Alcibar, Joel Pino +2Parametric Physics-Informed Neural Network Heat Diffusion
Department of Electronics and Computer Science Mondragon University Arrasate-Mondragon, 20500, Spain · CARTIF Technology Centre Valladolid, 47151, Spain · Department of Physical Chemistry University of the Basque Country (EHU)[cs.LG] Basque Centre for Applied Mathematics (BCAM) Leioa, 20018, Spain +1
FMA-Net++: Motion- and Exposure-Aware Joint Video Super-Resolution and Deblurring
Dec 4, 2025Geunhyuk Youk, Jihyong Oh, Munchurl KimGaussian Deblurring Video Restoration
KAIST, Republic of Korea · CMLab, Chung-Ang University, Republic of Korea
Scaling Textual Gradients via Sampling-Based Momentum
May 31, 2025Zixin Ding, Junyuan Hong, Zhan Shi +6Large Language Model Training Automatic Prompt Optimization
University of Chicago · University of Texas at Austin · Santa Clara University +3
When Auditors Fabricate: Batch-Size Degradation and Confident Hallucination in LLM Detection of Planted Document Contamination
Date pendingKaran Parekh, Sanjana Pendyala Ravinder, Sana Mhapsekar +1Large Language Model Evaluation Machine-Generated Text Detection
University of North Texas

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Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics

Yuhan Hu, Xiaolei Fang

Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA

Federated Learning Predictive Maintenance Early Failure Prediction Prognostics Degradation Privacy

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Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while keeping each client's data local and confidential. However, traditional federated models often assume homogeneity in the degradation processes across clients, an assumption that may not hold in many industrial settings. To overcome this, this paper proposes a personalized federated prognostic model designed to accommodate clients with heterogeneous degradation processes, allowing them to build tailored prognostic models. The prognostic model iteratively facilitates the underlying pairwise collaborations between clients with similar degradation patterns, which enhances the performance of personalized federated learning. To estimate parameters jointly using decentralized datasets, we develop a federated parameter estimation algorithm based on proximal gradient descent. The proposed approach addresses the limitations of existing federated prognostic models by simultaneously achieving model personalization, preserving data privacy, and providing comprehensive failure time distributions. The superiority of the proposed model is validated through extensive simulation studies and a case study using the turbofan engine degradation dataset from the NASA repository.

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