  In one-shot federated learning (FL), clients collaboratively train a global
model in a single round of communication. Existing approaches for one-shot FL
enhance communication efficiency at the expense of diminished accuracy. This
paper introduces FedPFT (Federated Learning with Parametric Feature Transfer),
a methodology that harnesses the transferability of foundation models to
enhance both accuracy and communication efficiency in one-shot FL. The approach
involves transferring per-client parametric models (specifically, Gaussian
mixtures) of features extracted from foundation models. Subsequently, each
parametric model is employed to generate synthetic features for training a
classifier head. Experimental results on eight datasets demonstrate that FedPFT
enhances the communication-accuracy frontier in both centralized and
decentralized FL scenarios, as well as across diverse data-heterogeneity
settings such as covariate shift and task shift, with improvements of up to
20.6%. Additionally, FedPFT adheres to the data minimization principle of FL,
as clients do not send real features. We demonstrate that sending real features
is vulnerable to potent reconstruction attacks. Moreover, we show that FedPFT
is amenable to formal privacy guarantees via differential privacy,
demonstrating favourable privacy-accuracy tradeoffs.
