2 citations · 6 across the 5 of their papers we have counts for
5 papers
FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacement
Fatima Z. Abacha, Sin G. Teo, Yuanxiang Wu +2
Federated Learning remains highly susceptible to backdoor attacks--malicious clients inject targeted behaviours into the global model. Existing defenses suffer from substantial fal…
Assessing Privacy Compliance of Android Third-Party SDKs
Mark Huasong Meng, Chuan Yan, Qing Zhang +5
Third-party Software Development Kits (SDKs) are widely adopted in Android app development, to effortlessly accelerate development pipelines and enhance app functionality. However,…
Synthetic Data Aided Federated Learning Using Foundation Models
Fatima Abacha, Sin G. Teo, Lucas C. Cordeiro +1
In heterogeneous scenarios where the data distribution amongst the Federated Learning (FL) participants is Non-Independent and Identically distributed (Non-IID), FL suffers from th…
PAODING: A High-fidelity Data-free Pruning Toolkit for Debloating Pre-trained Neural Networks
Mark Huasong Meng, Hao Guan, Liuhuo Wan +3
We present PAODING, a toolkit to debloat pretrained neural network models through the lens of data-free pruning. To preserve the model fidelity, PAODING adopts an iterative process…
Supervised Robustness-preserving Data-free Neural Network Pruning
Mark Huasong Meng, Guangdong Bai, Sin Gee Teo +1
When deploying pre-trained neural network models in real-world applications, model consumers often encounter resource-constraint platforms such as mobile and smart devices. They ty…