activity
20192026
most citedThe Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning

59 citations · 74 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Morphology-Aware Ambiguity Learning for Wafer Defect Decision Support

Seungjun Chu, Seokhyun Chung

Wafer map defect recognition is commonly formulated as a fixed-taxonomy classification problem that assigns each wafer to a single defect class. However, some wafers exhibit morpho…

cs.LG2026

Rethinking Personalized Reward Modeling for LLMs under Preference Heterogeneity via Group-Debiased Federated Learning

Seongyoon Kim, Boryeong Cho, Jihwan Oh +2

Large language models are increasingly aligned to human preferences via reward modeling, but user preference data are sensitive and often cannot be centralized. Federated learning…

cs.AI2025

Fed-Joint: Joint Modeling of Nonlinear Degradation Signals and Failure Events for Remaining Useful Life Prediction using Federated Learning

Cheoljoon Jeong, Xubo Yue, Seokhyun Chung

Many failure mechanisms of machinery are closely related to the behavior of condition monitoring (CM) signals. To achieve a cost-effective preventive maintenance strategy, accurate…

cs.LG2024★ 7 cited

Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes

Seokhyun Chung, Raed Al Kontar

Building a predictive model that rapidly adapts to real-time condition monitoring (CM) signals is critical for engineering systems/units. Unfortunately, many current methods suffer…

cs.LG2021★ 59 cited

The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning

Raed Kontar, Naichen Shi, Xubo Yue +12

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is br…

stat.ML2020★ 5 cited

Weakly-supervised Multi-output Regression via Correlated Gaussian Processes

Seokhyun Chung, Raed Al Kontar, Zhenke Wu

Multi-output regression seeks to borrow strength and leverage commonalities across different but related outputs in order to enhance learning and prediction accuracy. A fundamental…

stat.ML2019★ 3 cited

Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data

Seokhyun Chung, Raed Kontar

The advance of modern sensor technologies enables collection of multi-stream longitudinal data where multiple signals from different units are collected in real-time. In this artic…