13 citations · 29 across the 4 of their papers we have counts for
7 papers
Targeted Vaccine: Safety Alignment for Large Language Models against Harmful Fine-Tuning via Layer-wise Perturbation
Guozhi Liu, Weiwei Lin, Tiansheng Huang +3
Harmful fine-tuning attack poses a serious threat to the online fine-tuning service. Vaccine, a recent alignment-stage defense, applies uniform perturbation to all layers of embedd…
Fusion of Global and Local Knowledge for Personalized Federated Learning
Tiansheng Huang, Li Shen, Yan Sun +2
Personalized federated learning, as a variant of federated learning, trains customized models for clients using their heterogeneously distributed data. However, it is still inconcl…
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy
Yan Sun, Li Shen, Tiansheng Huang +2
Federated learning is an emerging distributed machine learning framework which jointly trains a global model via a large number of local devices with data privacy protections. Its…
Achieving Personalized Federated Learning with Sparse Local Models
Tiansheng Huang, Shiwei Liu, Li Shen +3
Federated learning (FL) is vulnerable to heterogeneously distributed data, since a common global model in FL may not adapt to the heterogeneous data distribution of each user. To c…
Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply
Tiansheng Huang, Weiwei Lin, Xiaobin Hong +5
With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery. Yet…
Stochastic Client Selection for Federated Learning with Volatile Clients
Tiansheng Huang, Weiwei Lin, Li Shen +2
Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public. In each round of synchronous FL training, only a…