217 citations · 1.8k across the 154 of their papers we have counts for
15 papers · 1 filter
AFLGuard: Byzantine-robust Asynchronous Federated Learning
Minghong Fang, Jia Liu, Neil Zhenqiang Gong +1
Federated learning (FL) is an emerging machine learning paradigm, in which clients jointly learn a model with the help of a cloud server. A fundamental challenge of FL is that the…
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning
Hongbin Liu, Wenjie Qu, Jinyuan Jia +1
Classifiers in supervised learning have various security and privacy issues, e.g., 1) data poisoning attacks, backdoor attacks, and adversarial examples on the security side as wel…
SoK: Secure Human-centered Wireless Sensing
Wei Sun, Tingjun Chen, Neil Gong
Human-centered wireless sensing (HCWS) aims to understand the fine-grained environment and activities of a human using the diverse wireless signals around him/her. While the sensed…
CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive Learning
Jinghuai Zhang, Hongbin Liu, Jinyuan Jia +1
Contrastive learning (CL) pre-trains general-purpose encoders using an unlabeled pre-training dataset, which consists of images or image-text pairs. CL is vulnerable to data poison…
Addressing Heterogeneity in Federated Learning via Distributional Transformation
Haolin Yuan, Bo Hui, Yuchen Yang +3
Federated learning (FL) allows multiple clients to collaboratively train a deep learning model. One major challenge of FL is when data distribution is heterogeneous, i.e., differs…
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information
Xiaoyu Cao, Jinyuan Jia, Zaixi Zhang +1
Federated learning is vulnerable to poisoning attacks in which malicious clients poison the global model via sending malicious model updates to the server. Existing defenses focus…