49 citations · 77 across the 7 of their papers we have counts for
7 papers
Reflex: Real-Time VLA Control through Streaming Inference
Yuanchun Guo, Bingyan Liu
Flow matching Vision-Language-Action (VLA) models promise precise continuous control, but their iterative denoising nature introduces fundamental incompatibilities with real-time r…
No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML
Ziqi Zhang, Chen Gong, Yifeng Cai +5
On-device ML introduces new security challenges: DNN models become white-box accessible to device users. Based on white-box information, adversaries can conduct effective model ste…
Recent Advances on Federated Learning: A Systematic Survey
Bingyan Liu, Nuoyan Lv, Yuanchun Guo +1
Federated learning has emerged as an effective paradigm to achieve privacy-preserving collaborative learning among different parties. Compared to traditional centralized learning t…
DistFL: Distribution-aware Federated Learning for Mobile Scenarios
Bingyan Liu, Yifeng Cai, Ziqi Zhang +5
Federated learning (FL) has emerged as an effective solution to decentralized and privacy-preserving machine learning for mobile clients. While traditional FL has demonstrated its…
ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection
Yuanchun Li, Ziqi Zhang, Bingyan Liu +2
The knowledge of a deep learning model may be transferred to a student model, leading to intellectual property infringement or vulnerability propagation. Detecting such knowledge r…
PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization
Bingyan Liu, Yao Guo, Xiangqun Chen
Federated learning (FL) has become a prevalent distributed machine learning paradigm with improved privacy. After learning, the resulting federated model should be further personal…
TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning
Bingyan Liu, Yifeng Cai, Yao Guo +1
The increasing of pre-trained models has significantly facilitated the performance on limited data tasks with transfer learning. However, progress on transfer learning mainly focus…