A comparative analysis of three federated LLM fine-tuning frameworks shows FedLLMs achieve highest accuracy, KD-FedLLMs highest client computation, and Split-FedLLMs highest communication overhead in a GPT-2/Banking77 case study.
Parameter-efficient fine-tuning of large-scale pre-trained language models,
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Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions
A comparative analysis of three federated LLM fine-tuning frameworks shows FedLLMs achieve highest accuracy, KD-FedLLMs highest client computation, and Split-FedLLMs highest communication overhead in a GPT-2/Banking77 case study.