PEP-FedPT achieves generalization and personalization in federated ViT prompt tuning via adaptive mixing of class-specific prompts weighted by global class prototypes and client priors, without per-client trainable parameters.
Herel(θ, (x,y ))is loss evaluated on the sample (x,y)andf k(θ)is expectation across the samples drawn fromDk
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Prompt Estimation from Prototypes for Federated Prompt Tuning of Vision Transformers
PEP-FedPT achieves generalization and personalization in federated ViT prompt tuning via adaptive mixing of class-specific prompts weighted by global class prototypes and client priors, without per-client trainable parameters.