  Quick global aggregation of effective distributed parameters is crucial to
federated learning (FL), which requires adequate bandwidth for parameters
communication and sufficient user data for local training. Otherwise, FL may
cost excessive training time for convergence and produce inaccurate models. In
this paper, we propose a brand-new FL framework, PromptFL, that replaces the
federated model training with the federated prompt training, i.e., let
federated participants train prompts instead of a shared model, to
simultaneously achieve the efficient global aggregation and local training on
insufficient data by exploiting the power of foundation models (FM) in a
distributed way. PromptFL ships an off-the-shelf FM, i.e., CLIP, to distributed
clients who would cooperatively train shared soft prompts based on very few
local data. Since PromptFL only needs to update the prompts instead of the
whole model, both the local training and the global aggregation can be
significantly accelerated. And FM trained over large scale data can provide
strong adaptation capability to distributed users tasks with the trained soft
prompts. We empirically analyze the PromptFL via extensive experiments, and
show its superiority in terms of system feasibility, user privacy, and
performance.
