2 citations · 5 across the 14 of their papers we have counts for
15 papers
Measurement-Budget Allocation in Quantum Learning with Finite-Shot Generalization Guarantees
Ferhat Ozgur Catak
On near-term quantum hardware, estimating a Born probability requires repeated circuit executions. A quantum learning experiment with a fixed measurement budget must therefore…
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…
Logit-Level Uncertainty Quantification in Vision-Language Models for Histopathology Image Analysis
Betul Yurdem, Ferhat Ozgur Catak, Murat Kuzlu +1
Vision-Language Models (VLMs) with their multimodal capabilities have demonstrated remarkable success in almost all domains, including education, transportation, healthcare, energy…
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…
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…
Comparative Analysis of Attention Mechanisms for Automatic Modulation Classification in Radio Frequency Signals
Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali
Automatic Modulation Classification (AMC) is a critical component in cognitive radio systems and spectrum management applications. This study presents a comprehensive comparative a…
Building Trust in Healthcare with Privacy Techniques: Blockchain in the Cloud
Ferhat Ozgur Catak, Chunming Rong, Øyvind Meinich-Bache +2
This study introduces a cutting-edge architecture developed for the NewbornTime project, which uses advanced AI to analyze video data at birth and during newborn resuscitation, wit…
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…
BERT4MIMO: A Foundation Model using BERT Architecture for Massive MIMO Channel State Information Prediction
Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali
Massive MIMO (Multiple-Input Multiple-Output) is an advanced wireless communication technology, using a large number of antennas to improve the overall performance of the communica…
Improving Medical Diagnostics with Vision-Language Models: Convex Hull-Based Uncertainty Analysis
Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick
In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. Howeve…
Neural Networks Meet Elliptic Curve Cryptography: A Novel Approach to Secure Communication
Mina Cecilie Wøien, Ferhat Ozgur Catak, Murat Kuzlu +1
In recent years, neural networks have been used to implement symmetric cryptographic functions for secure communications. Extending this domain, the proposed approach explores the…
Uncertainty Quantification in Large Language Models Through Convex Hull Analysis
Ferhat Ozgur Catak, Murat Kuzlu
Uncertainty quantification approaches have been more critical in large language models (LLMs), particularly high-risk applications requiring reliable outputs. However, traditional…
Classification with Extreme Learning Machine and Ensemble Algorithms Over Randomly Partitioned Data
Ferhat Özgür Çatak
In this age of Big Data, machine learning based data mining methods are extensively used to inspect large scale data sets. Deriving applicable predictive modeling from these type o…