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Parametric Feature Transfer: One-shot Federated Learning with Foundation Models
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.
A Theory of Non-Acyclic Generative Flow Networks
GFlowNets is a novel flow-based method for learning a stochastic policy to generate objects via a sequence of actions and with probability proportional to a given positive reward.
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