How does varying LoRA rank impact cross-domain transfer accuracy on GLUE semantic textual similarity tasks und

Abstract

Abstract: Foundation models (FMs) achieve strong performance across diverse tasks with task-specific fine-tuning, yet full parameter fine-tuning is often computationally prohibitive for large models. Parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaptation (LoRA) reduce this cost by introducing low-rank matrices for tuning fewer parameters. While LoRA allows for efficient fine-tuning, it requires significant data for adaptation, making Federated Learning (FL) an appealing solution due to its privacy-preserving collaborative framework. However, combining LoRA with FL introduces two key cha

Research Question

How does varying LoRA rank impact cross-domain transfer accuracy on GLUE semantic textual similarity tasks under differential privacy constraints?

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