3 citations · 6 across the 5 of their papers we have counts for
7 papers
Bias Similarity Measurement: A Black-Box Audit of Fairness Across LLMs
Hyejun Jeong, Shiqing Ma, Amir Houmansadr
Large Language Models (LLMs) reproduce social biases, yet prevailing evaluations score models in isolation, obscuring how biases persist across families and releases. We introduce…
A Survey on Federated Unlearning: Challenges and Opportunities
Hyejun Jeong, Shiqing Ma, Amir Houmansadr
Federated learning (FL), introduced in 2017, facilitates collaborative learning between non-trusting parties with no need for the parties to explicitly share their data among thems…
Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare
Hyejun Jeong, Tai-Myoung Chung
The advent of Federated Learning has enabled the creation of a high-performing model as if it had been trained on a considerable amount of data. A multitude of participants and a s…
FedCC: Robust Federated Learning against Model Poisoning Attacks
Hyejun Jeong, Hamin Son, Seohu Lee +2
Federated learning is a distributed framework designed to address privacy concerns. However, it introduces new attack surfaces, which are especially prone when data is non-Independ…
Federated Learning: Issues in Medical Application
Joo Hun Yoo, Hyejun Jeong, Jaehyeok Lee +1
Since the federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in the f…
Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application
Joo Hun Yoo, Ha Min Son, Hyejun Jeong +5
While machine learning techniques are being applied to various fields for their exceptional ability to find complex relations in large datasets, the strengthening of regulations on…
ABC-FL: Anomalous and Benign client Classification in Federated Learning
Hyejun Jeong, Joonyong Hwang, Tai Myung Chung
Federated Learning is a distributed machine learning framework designed for data privacy preservation i.e., local data remain private throughout the entire training and testing pro…