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Beam Profiling and Beamforming Modeling for mmWave NextG Networks
The datasets we have obtained from the beam profiling and the machine learning model for beamforming are valuable for a broad set of network design problems, such as network topology optimization, user equipment association, power allocation, and beam scheduling, in complex and dynamic mmWave networks.
Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference
The prevalent use of Transformer-like models, exemplified by ChatGPT in modern language processing applications, underscores the critical need for enabling private inference essential for many cloud-based services reliant on such models.
DeepAuditor: Distributed Online Intrusion Detection System for IoT devices via Power Side-channel Auditing
We also designed a distributed CNN classifier for online inference in a laboratory setting.
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks
Based on this observation, we propose a novel defense mechanism to pinpoint a malicious pocket by "wrapping" them into a tight convex hull in the feature space.
CHEETAH: An Ultra-Fast, Approximation-Free, and Privacy-Preserved Neural Network Framework based on Joint Obscure Linear and Nonlinear Computations
This significant speedup enables a wide range of practical applications based on privacy-preserved deep neural networks.
