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Quick global aggregation of effective distributed parameters is crucial tofederated learning (FL), which requires adequate bandwidth for parameterscommunication and sufficient user data for local training. Otherwise, FL maycost excessive training time for convergence and produce inaccurate models. Inthis paper, we propose a brand-new FL framework, PromptFL, that replaces thefederated model training with the federated prompt training, i.e., letfederated participants train prompts instead of a shared model, tosimultaneously achieve the efficient global aggregation and local training oninsufficient data by exploiting the power of foundation models (FM) in adistributed way. PromptFL ships an off-the-shelf FM, i.e., CLIP, to distributedclients who would cooperatively train shared soft prompts based on very fewlocal data. Since PromptFL only needs to update the prompts instead of thewhole model, both the local training and the global aggregation can besignificantly accelerated. And FM trained over large scale data can providestrong adaptation capability to distributed users tasks with the trained softprompts. We empirically analyze the PromptFL via extensive experiments, andshow its superiority in terms of system feasibility, user privacy, andperformance.

PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Model