4 papers
BoHA: Blockwise Hadamard Product Adaptation for Parameter-Efficient Fine-Tuning
Feng Yu, Jia Hu, Geyong Min
Parameter-efficient fine-tuning (PEFT) of large language models trains a small task-specific parameter set while keeping the pretrained model frozen. The dominant Low-Rank Adaptati…
Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning
Feng Yu, Jia Hu, Geyong Min
Federated Parameter-Efficient Fine-Tuning (Fed-PEFT) enables lightweight adaptation of large pre-trained models in federated learning settings by updating only a small subset of pa…
SpeechForensics: Audio-Visual Speech Representation Learning for Face Forgery Detection
Yachao Liang, Min Yu, Gang Li +6
Detection of face forgery videos remains a formidable challenge in the field of digital forensics, especially the generalization to unseen datasets and common perturbations. In thi…
Federated Continual Learning for Edge-AI: A Comprehensive Survey
Zi Wang, Fei Wu, Feng Yu +3
Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge,…