2 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
EvalQReason: A Framework for Step-Level Reasoning Evaluation in Large Language Models
Shaima Ahmad Freja, Ferhat Ozgur Catak, Betul Yurdem +1
Large Language Models (LLMs) are increasingly deployed in critical applications requiring reliable reasoning, yet their internal reasoning processes remain difficult to evaluate sy…
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…