4 papers
Federated Unlearning in the Wild: Rethinking Fairness and Data Discrepancy
ZiHeng Huang, Di Wu, Jun Bai +4
Machine unlearning is critical for enforcing data deletion rights like the "right to be forgotten." As a decentralized paradigm, Federated Learning (FL) also requires unlearning, b…
FEAT: A Multi-Agent Forensic AI System with Domain-Adapted Large Language Model for Automated Cause-of-Death Analysis
Chen Shen, Wanqing Zhang, Kehan Li +17
Forensic cause-of-death determination faces systemic challenges, including workforce shortages and diagnostic variability, particularly in high-volume systems like China's medicole…
FedMLAC: Mutual Learning Driven Heterogeneous Federated Audio Classification
Jun Bai, Rajib Rana, Di Wu +5
Federated Learning (FL) offers a privacy-preserving framework for training audio classification (AC) models across decentralized clients without sharing raw data. However, Federate…
A Comprehensive Survey on Machine Learning Driven Material Defect Detection
Jun Bai, Di Wu, Tristan Shelley +5
Material defects (MD) represent a primary challenge affecting product performance and giving rise to safety issues in related products. The rapid and accurate identification and lo…