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cs.LG2025
Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
Wenkai Guo, Xuefeng Liu, Haolin Wang +3
Fine-tuning large language models (LLMs) with local data is a widely adopted approach for organizations seeking to adapt LLMs to their specific domains. Given the shared characteri…
cs.LG2025
Empowering LLMs in Decision Games through Algorithmic Data Synthesis
Haolin Wang, Xueyan Li, Yazhe Niu +2
Large Language Models (LLMs) have exhibited impressive capabilities across numerous domains, yet they often struggle with complex reasoning and decision-making tasks. Decision-maki…
cs.LG2024★ 1 cited
Why Go Full? Elevating Federated Learning Through Partial Network Updates
Haolin Wang, Xuefeng Liu, Jianwei Niu +2
Federated learning is a distributed machine learning paradigm designed to protect user data privacy, which has been successfully implemented across various scenarios. In traditiona…