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Author

Se-Hoon Jung

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Review Open access 2026

Non-IID Data Challenges in Federated Learning for Distributed Systems: A Systematic Review

: Federated learning is a distributed machine learning framework, that enhances privacy and security against malicious behavior, including data poisoning and model poisoning. Nevertheless, federated learning models tend to have worse performance than centralized models, particularly when the data is non-independent and...

Indra Kumari, Se-Hoon Jung, Chun-Bo Sim et al. · 0 citations
Open access Aug 2026

WindHCT: context and local hybrid CNN Transformer for wind speed super-resolution

Global climate models operate at coarse spatial resolutions that limit their ability to represent localized atmospheric phenomena, motivating deep learning–based super-resolution (SR) for meteorological fields. However, most existing SR models are designed for natural images and fail to fully capture the characteristic...

Won-ji Jo, Sung-Wook Park, Y. Park et al. · 0 citations
Open access 2026

MAD-Based Update Filtering for Non-IID Federated Learning: Robustness Analysis Under Poisoning Attacks

A ablation indicates that removing MAD filtering substantially reduces class-balanced performance on HAM10000 under noise injection, and a diagnostic analysis shows that the cosine-distance signal separates benign and malicious updates strongly under noise injection but weakly under sign flipping.

Tae-Wook Kang, Ji-Woo Park, Chulyoung Park et al. · 0 citations

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