4 papers
FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning
Binghui Zhang, Luis Mares De La Cruz, Binghui Wang
Federated Learning (FL) is an emerging decentralized learning paradigm that can partly address the privacy concern that cannot be handled by traditional centralized and distributed…
Learning Robust and Privacy-Preserving Representations via Information Theory
Binghui Zhang, Sayedeh Leila Noorbakhsh, Yun Dong +2
Machine learning models are vulnerable to both security attacks (e.g., adversarial examples) and privacy attacks (e.g., private attribute inference). We take the first step to miti…
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective
Zifan Wang, Binghui Zhang, Meng Pang +2
Federated learning (FL) is an emerging collaborative learning paradigm that aims to protect data privacy. Unfortunately, recent works show FL algorithms are vulnerable to the serio…
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks
Sayedeh Leila Noorbakhsh, Binghui Zhang, Yuan Hong +1
Machine learning (ML) is vulnerable to inference (e.g., membership inference, property inference, and data reconstruction) attacks that aim to infer the private information of trai…