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Found 3 papers, 2 papers with code
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models
FATE-LLM (1) facilitates federated learning for large language models (coined FedLLM); (2) promotes efficient training of FedLLM using parameter-efficient fine-tuning methods; (3) protects the intellectual property of LLMs; (4) preserves data privacy during training and inference through privacy-preserving mechanisms.
Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability
We present a novel privacy-preserving federated adversarial domain adaptation approach ($\textbf{PrADA}$) to address an under-studied but practical cross-silo federated domain adaptation problem, in which the party of the target domain is insufficient in both samples and features.
SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning
Gradient boosting decision tree (GBDT) is a widely used ensemble algorithm in the industry.
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