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Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity
Large Language Models (LLMs) have achieved remarkable success across diverse domains, but their adaptation to privacy-sensitive, distributed datasets remains a challenge. While Federated Learning (FL) combined with Low-Rank Adaptation (LoRA) provides a resource-efficient paradigm for collaborative f
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