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…
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…
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…
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…