  End users face a choice between privacy and efficiency in current Large
Language Model (LLM) service paradigms. In cloud-based paradigms, users are
forced to compromise data locality for generation quality and processing speed.
Conversely, edge device paradigms maintain data locality but fail to deliver
satisfactory performance. In this work, we propose a novel LLM service paradigm
that distributes privacy-sensitive computation on edge devices and shared
computation in the cloud. Only activations are transmitted between the central
cloud and edge devices to ensure data locality. Our core innovation,
PrivateLoRA, addresses the challenging communication overhead by exploiting the
low rank of residual activations, achieving over 95% communication reduction.
Consequently, PrivateLoRA effectively maintains data locality and is extremely
resource efficient. Under standard 5G networks, PrivateLoRA achieves throughput
over 300% of device-only solutions for 7B models and over 80% of an A100 GPU
for 33B models. PrivateLoRA also provides tuning performance comparable to LoRA
for advanced personalization. Our approach democratizes access to
state-of-the-art generative AI for edge devices, paving the way for more
tailored LLM experiences for the general public. To our knowledge, our proposed
framework is the first efficient and privacy-preserving LLM solution in the
literature.
