<ul><li>Low-rank adaptation methods in natural language processing, such as LoRA and FLoRA, involve keeping pre-trained model weights fixed and incorporating trainable low-rank decomposition matrices into some layers of the transformer architecture, called adapters.</li><li>Researchers have found that the low-rank adaptation used in LoRA and FLoRA introduces random noise into the batch gradients with respect to the adapter parameters, leading to a variance in the injected noise that increases as the adaptation rank decreases.</li><li>The study establishes a relationship between low-rank adaptation and differential privacy, showing that the dynamics of low-rank adaptation is similar to differentially private fine-tuning of the adapters.</li><li>The researchers suggest that low-rank adaptation offers privacy protection without the high space complexity of differentially private stochastic gradient descent (DPSGD), providing an efficient alternative for privacy-preserving fine-tuning in NLP models.</li></ul>

On the Implicit Relation Between Low-Rank Adaptation and Differential Privacy

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