4 papers
Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Models
Yingqi Hu, Zhuo Zhang, Jingyuan Zhang +4
Federated large language models (FedLLMs) enable cross-silo collaborative training among institutions while preserving data locality, making them appealing for privacy-sensitive do…
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…
From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling
Yi Hu, Hanchi Ren, Jingjing Deng +1
Stock price prediction is a critical area of financial forecasting, traditionally approached by training models using the historical price data of individual stocks. While these mo…
Gradients Stand-in for Defending Deep Leakage in Federated Learning
H. Yi, H. Ren, C. Hu +3
Federated Learning (FL) has become a cornerstone of privacy protection, shifting the paradigm towards localizing sensitive data while only sending model gradients to a central serv…