2 citations · 4 across the 4 of their papers we have counts for
5 papers
Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis
Hong Huang, Dapeng Wu
Large language models (LLMs) have made exciting achievements across various domains, yet their deployment on resource-constrained personal devices remains hindered by the prohibiti…
FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
Hong Huang, Hai Yang, Yuan Chen +2
Federated Learning (FL) enables collaborative model training across distributed clients without data sharing, but its high computational and communication demands strain resource-c…
FedMef: Towards Memory-efficient Federated Dynamic Pruning
Hong Huang, Weiming Zhuang, Chen Chen +1
Federated learning (FL) promotes decentralized training while prioritizing data confidentiality. However, its application on resource-constrained devices is challenging due to the…
Mitigating Cross-client GANs-based Attack in Federated Learning
Hong Huang, Xinyu Lei, Tao Xiang
Machine learning makes multimedia data (e.g., images) more attractive, however, multimedia data is usually distributed and privacy sensitive. Multiple distributed multimedia client…
Distributed Pruning Towards Tiny Neural Networks in Federated Learning
Hong Huang, Lan Zhang, Chaoyue Sun +3
Neural network pruning is an essential technique for reducing the size and complexity of deep neural networks, enabling large-scale models on devices with limited resources. Howeve…