33 citations · 60 across the 5 of their papers we have counts for
6 papers
FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion
Tao Fan, Guoqiang Ma, Yuanfeng Song +3
Federated fine-tuning of Large Language Models (LLMs) is obstructed by a trilemma of challenges: protecting LLMs intellectual property (IP), ensuring client privacy, and mitigating…
PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation
Tao Fan, Guoqiang Ma, Yuanfeng Song +2
Compressing Large Language Models (LLMs) into task-specific Small Language Models (SLMs) encounters two significant challenges: safeguarding domain-specific knowledge privacy and m…
Federated Co-tuning Framework for Large and Small Language Models
Tao Fan, Yan Kang, Guoqiang Ma +4
By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the capabilities of LLMs. Nonetheless, a ga…
FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models
Tao Fan, Guoqiang Ma, Yan Kang +5
Recent research in federated large language models (LLMs) has primarily focused on enabling clients to fine-tune their locally deployed homogeneous LLMs collaboratively or on trans…
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models
Tao Fan, Yan Kang, Guoqiang Ma +4
Large Language Models (LLMs), such as ChatGPT, LLaMA, GLM, and PaLM, have exhibited remarkable performances across various tasks in recent years. However, LLMs face two main challe…
SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree
Tao Fan, Weijing Chen, Guoqiang Ma +3
Gradient boosting decision tree (GBDT) is an ensemble machine learning algorithm, which is widely used in industry, due to its good performance and easy interpretation. Due to the…