6 citations · 14 across the 30 of their papers we have counts for
5 papers · 1 filter
Extracting Spatiotemporal Data from Gradients with Large Language Models
Lele Zheng, Yang Cao, Renhe Jiang +4
Recent works show that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning. While success was demonstrated primar…
Investigating Effective Speaker Property Privacy Protection in Federated Learning for Speech Emotion Recognition
Chao Tan, Sheng Li, Yang Cao +2
Federated Learning (FL) is a privacy-preserving approach that allows servers to aggregate distributed models transmitted from local clients rather than training on user data. More…
Benchmarking Japanese Speech Recognition on ASR-LLM Setups with Multi-Pass Augmented Generative Error Correction
Yuka Ko, Sheng Li, Chao-Han Huck Yang +1
With the strong representational power of large language models (LLMs), generative error correction (GER) for automatic speech recognition (ASR) aims to provide semantic and phonet…
Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks
Lele Zheng, Yang Cao, Renhe Jiang +4
Spatiotemporal federated learning has recently raised intensive studies due to its ability to train valuable models with only shared gradients in various location-based services. O…
MOS-FAD: Improving Fake Audio Detection Via Automatic Mean Opinion Score Prediction
Wangjin Zhou, Zhengdong Yang, Chenhui Chu +4
Automatic Mean Opinion Score (MOS) prediction is employed to evaluate the quality of synthetic speech. This study extends the application of predicted MOS to the task of Fake Audio…