  With the blowout development of pre-trained models (PTMs), the efficient
tuning of these models for diverse downstream applications has emerged as a
pivotal research concern. Although recent investigations into prompt tuning
have provided promising avenues, three salient challenges persist: (1) memory
constraint: the continuous growth in the size of open-source PTMs renders
fine-tuning, even a fraction of their parameters, challenging for many
practitioners. (2) model privacy: existing PTMs often function as public API
services, with their parameters inaccessible for effective or tailored
fine-tuning. (3) data privacy: the fine-tuning of PTMs necessitates
high-quality datasets, which are typically localized and not shared to public.
To optimally harness each local dataset while navigating memory constraints and
preserving privacy, we propose Federated Black-Box Prompt Tuning (Fed-BBPT).
This innovative approach eschews reliance on parameter architectures and
private dataset access, instead capitalizing on a central server that aids
local users in collaboratively training a prompt generator through regular
aggregation. Local users leverage API-driven learning via a zero-order
optimizer, obviating the need for PTM deployment. Relative to extensive
fine-tuning, Fed-BBPT proficiently sidesteps memory challenges tied to PTM
storage and fine-tuning on local machines, tapping into comprehensive,
high-quality, yet private training datasets. A thorough evaluation across 40
datasets spanning CV and NLP tasks underscores the robustness of our proposed
model.
