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Found 4 papers, 3 papers with code
AgentScope: A Flexible yet Robust Multi-Agent Platform
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs).
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes
Pre-trained large language models (LLMs) need fine-tuning to improve their responsiveness to natural language instructions.
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning
When several entities have similar interested tasks, but their data cannot be shared because of privacy concerns regulations, federated learning (FL) is a mainstream solution to leverage the data of different entities.
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