2 citations · 3 across the 8 of their papers we have counts for
10 papers
When Trees Are Not Enough: Learning Mixed-Topology Feature Graphs with Adaptive Graph Sparse Autoencoders
Xiaozuo Shen, Yifei Cai, Tian Tan +3
Sparse autoencoders (SAEs) expose interpretable features in large language model activations, yet existing structured SAEs impose single-parent trees or forests, while post-hoc gra…
DegreeSpar: Structured Degree Sparsity for Efficient Secure Transformer Inference
Yifei Cai, Zhuoran Li, Xiaozuo Shen +2
Secure Transformer inference protects sensitive inputs but incurs substantial cryptographic overhead, with nonlinear operations such as Softmax and GeLU becoming major bottlenecks.…
SecDTD: Dynamic Token Drop for Secure Transformers Inference
Yifei Cai, Zhuoran Li, Yizhou Feng +4
The rapid adoption of Transformer-based AI has been driven by accessible models such as ChatGPT, which provide API-based services for developers and businesses. However, as these o…
DF-LoGiT: Data-Free Logic-Gated Backdoor Attacks in Vision Transformers
Xiaozuo Shen, Yifei Cai, Rui Ning +2
The widespread adoption of Vision Transformers (ViTs) elevates supply-chain risk on third-party model hubs, where an adversary can implant backdoors into released checkpoints. Exis…
RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance
Miao Lin, Feng Yu, Rui Ning +6
Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the pervasive class imbalance in real-world sce…
Towards Zero Rotation and Beyond: Architecting Neural Networks for Fast Secure Inference with Homomorphic Encryption
Yifei Cai, Yizhou Feng, Qiao Zhang +2
Privacy-preserving deep learning addresses privacy concerns in Machine Learning as a Service (MLaaS) by using Homomorphic Encryption (HE) for linear computations. However, the comp…
Beam Profiling and Beamforming Modeling for mmWave NextG Networks
Efat Samir Fathalla, Sahar Zargarzadeh, Chunsheng Xin +5
This paper presents an experimental study on mmWave beam profiling on a mmWave testbed, and develops a machine learning model for beamforming based on the experiment data. The data…
Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference
Xiangrui Xu, Qiao Zhang, Rui Ning +2
The prevalent use of Transformer-like models, exemplified by ChatGPT in modern language processing applications, underscores the critical need for enabling private inference essent…
Joint Linear and Nonlinear Computation across Functions for Efficient Privacy-Preserving Neural Network Inference
Qiao Zhang, Tao Xiang, Chunsheng Xin +2
While it is encouraging to witness the recent development in privacy-preserving Machine Learning as a Service (MLaaS), there still exists a significant performance gap for its depl…
Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running
Cong Wang, Bin Hu, Hongyi Wu
Energy is an essential, but often forgotten aspect in large-scale federated systems. As most of the research focuses on tackling computational and statistical heterogeneity from th…