Federated learning

Provable and Measurable Machine Unlearning in Modern Learning Systems

Cheng-Long Wang, Ph.D. Student, Computer Science

Mar 10, 10:30 - 12:30

B2 L5 R5209

Machine Unlearning Data Privacy Trustworthy AI Federated learning machine learning AI

This dissertation examines the foundations of machine unlearning under realistic learning system constraints and proposes both theoretically grounded unlearning algorithms and principled evaluation frameworks for modern learning systems.

Reem Arnaout

Intern, King Abdullah University of Science and Technology

wireless communication Signal processing Radio Frequency Localization Differential privacy Federated learning Network and Software Security

Visiting student at King Abdullah University of Science and Technology (KAUST).

KAUST-CEMSE-CS-PhD-Dissertation-Defense-Lijie-Zihang Xiang-Modern-Privacy-preserving-Machine-Learning

Modern Privacy-preserving Machine Learning: Rigorous Approach for Data Privacy

Zihang Xiang, Ph.D. Student, Computer Science

Jul 6, 10:00 - 12:00

B3 L5 R5216

privacy-preserving machine learning Differential privacy Federated learning

This dissertation centers around privacy-preserving technologies (differential privacy) in broad machine learning applications. This dissertation focuses on two sides of differential privacy: 1) designing privacy-preserving algorithms, 2) ensuring the falsifiability of privacy claims.

Cheng-Long Wang

Ph.D. Student, Computer Science

Machine Unlearning Data Privacy Trustworthy AI Federated learning

Cheng-Long Wang is a PhD candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), advised by Prof. Di Wang.