177 citations · 352 across the 12 of their papers we have counts for
13 papers
Ten Challenging Problems in Federated Foundation Models
Tao Fan, Hanlin Gu, Xuemei Cao +30
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of fed…
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…
FedCoT: Federated Chain-of-Thought Distillation for Large Language Models
Tao Fan, Weijing Chen, Yan Kang +5
Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, demonstrating exceptional proficiency across various tasks. However, their deploymen…
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…
Grounding Foundation Models through Federated Transfer Learning: A General Framework
Yan Kang, Tao Fan, Hanlin Gu +3
Foundation Models (FMs) such as GPT-4 encoded with vast knowledge and powerful emergent abilities have achieved remarkable success in various natural language processing and comput…
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 Hyperparameter Tuning via Multi-Objective Federated Learning
Ziyao Ren, Yan Kang, Lixin Fan +3
SecureBoost is a tree-boosting algorithm leveraging homomorphic encryption to protect data privacy in vertical federated learning setting. It is widely used in fields such as finan…
Accelerating Vertical Federated Learning
Dongqi Cai, Tao Fan, Yan Kang +4
Privacy, security and data governance constraints rule out a brute force process in the integration of cross-silo data, which inherits the development of the Internet of Things. Fe…
Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability
Yan Kang, Yang Liu, Yuezhou Wu +2
We present a novel privacy-preserving federated adversarial domain adaptation approach () to address an under-studied but practical cross-silo federated domain adap…
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…
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
Kai Yang, Tao Fan, Tianjian Chen +2
Data privacy and security becomes a major concern in building machine learning models from different data providers. Federated learning shows promise by leaving data at providers l…
SecureBoost: A Lossless Federated Learning Framework
Kewei Cheng, Tao Fan, Yilun Jin +4
The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (…