2 citations · 2 across the 7 of their papers we have counts for
7 papers
Quantum Key Distribution Secured Federated Learning for Channel Estimation and Radar Spectrum Sensing in 6G Networks
Ferhat Ozgur Catak, Murat Kuzlu, Jungwon Seo +1
This paper presents a federated learning framework secured by quantum key distribution (QKD) for wireless channel estimation and radar spectrum sensing in the next generation netwo…
Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong +1
Federated Inference (FI) studies how independently trained and privately owned models can collaborate at inference time without sharing data or model parameters. While recent work…
Trustworthy Quantum Machine Learning: A Roadmap for Reliability, Robustness, and Security in the NISQ Era
Ferhat Ozgur Catak, Jungwon Seo, Umit Cali
Quantum machine learning (QML) is a promising paradigm for tackling computational problems that challenge classical AI. Yet, the inherent probabilistic behavior of quantum mechanic…
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong +2
Federated Learning (FL) enables privacy-preserving multi-source information fusion (MSIF) but is challenged by client drift in highly heterogeneous data settings. Many existing dri…
Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study
Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong
As privacy concerns and data regulations grow, federated learning (FL) has emerged as a promising approach for training machine learning models across decentralized data sources wi…
ProxyLLM : LLM-Driven Framework for Customer Support Through Text-Style Transfer
Sehyeong Jo, Jungwon Seo
Chatbot-based customer support services have significantly advanced with the introduction of large language models (LLMs), enabling enhanced response quality and broader applicatio…
FedShift: Robust Federated Learning Aggregation Scheme in Resource Constrained Environment via Weight Shifting
Jungwon Seo, Minhoe Kim, Chunming Rong
Federated Learning (FL) commonly relies on a central server to coordinate training across distributed clients. While effective, this paradigm suffers from significant communication…