7 papers
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…