2 citations · 4 across the 4 of their papers we have counts for
4 papers · 1 filter
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…
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…