12 citations · 12 across the 8 of their papers we have counts for
10 papers
Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learning
Youngjoon Lee, Hyukjoon Lee, Seungrok Jung +4
Federated Learning (FL) facilitates decentralized collaborative learning without transmitting raw data. However, reliance on fixed global rounds or validation data for hyperparamet…
Privacy on the Fly: A Predictive Adversarial Transformation Network for Mobile Sensor Data
Tianle Song, Chenhao Lin, Yang Cao +5
Mobile motion sensors such as accelerometers and gyroscopes are now ubiquitously accessible by third-party apps via standard APIs. While enabling rich functionalities like activity…
When to Stop Federated Learning: Zero-Shot Generation of Synthetic Validation Data with Generative AI for Early Stopping
Youngjoon Lee, Hyukjoon Lee, Jinu Gong +2
Federated Learning (FL) enables collaborative model training across decentralized devices while preserving data privacy. However, FL methods typically run for a predefined number o…
Adaptive Token-Weighted Differential Privacy for LLMs: Not All Tokens Require Equal Protection
Manjiang Yu, Priyanka Singh, Xue Li +1
Large language models (LLMs) frequently memorize sensitive or personal information, raising significant privacy concerns. Existing variants of differential privacy stochastic gradi…
Debunking Optimization Myths in Federated Learning for Medical Image Classification
Youngjoon Lee, Hyukjoon Lee, Jinu Gong +2
Federated Learning (FL) is a collaborative learning method that enables decentralized model training while preserving data privacy. Despite its promise in medical imaging, recent F…
Robust Bandwidth Estimation for Real-Time Communication with Offline Reinforcement Learning
Jian Kai, Tianwei Zhang, Zihan Ling +2
Accurate bandwidth estimation (BWE) is critical for real-time communication (RTC) systems. Traditional heuristic approaches offer limited adaptability under dynamic networks, while…
A Decade of Metric Differential Privacy: Advancements and Applications
Xinpeng Xie, Chenyang Yu, Yan Huang +2
Metric Differential Privacy (mDP) builds upon the core principles of Differential Privacy (DP) by incorporating various distance metrics, which offer adaptable and context-sensitiv…
Tackling Data Heterogeneity in Federated Time Series Forecasting
Wei Yuan, Guanhua Ye, Xiangyu Zhao +3
Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting.…
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
Saber Malekmohammadi, Yaoliang Yu, Yang Cao
High utility and rigorous data privacy are of the main goals of a federated learning (FL) system, which learns a model from the data distributed among some clients. The latter has…
Differentially Private Federated Learning: A Systematic Review
Jie Fu, Yuan Hong, Xinpeng Ling +6
In recent years, privacy and security concerns in machine learning have promoted trusted federated learning to the forefront of research. Differential privacy has emerged as the de…