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Found 2 papers, 1 papers with code
A Framework for Cost-Effective and Self-Adaptive LLM Shaking and Recovery Mechanism
As Large Language Models (LLMs) gain great success in real-world applications, an increasing number of users are seeking to develop and deploy their customized LLMs through cloud services.
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
This paper proposes a Parameter-efficient prompt Tuning approach with Adaptive Optimization, i. e., FedPepTAO, to enable efficient and effective FL of LLMs.
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