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Found 3 papers, 1 papers with code
Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research.
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields.
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation
FedCOG consists of two key components at the client side: complementary data generation, which generates data extracted from the shared global model to complement the original dataset, and knowledge-distillation-based model training, which distills knowledge from global model to local model based on the generated data to mitigate over-fitting the original heterogeneous dataset.
