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Found 5 papers, 3 papers with code
Grounding Foundation Models through Federated Transfer Learning: A General Framework
Motivated by the strong growth in FTL-FM research and the potential impact of FTL-FM on industrial applications, we propose an FTL-FM framework that formulates problems of grounding FMs in the federated learning setting, construct a detailed taxonomy based on the FTL-FM framework to categorize state-of-the-art FTL-FM works, and comprehensively overview FTL-FM works based on the proposed taxonomy.
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.
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.
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
Our approach can considerably reduce the number of communication rounds with a little additional communication cost per round.
SecureBoost: A Lossless Federated Learning Framework
This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set.
