FedRef: Communication-Efficient Bayesian Fine-Tuning using a Reference Model
PositiveArtificial Intelligence
FedRef: Communication-Efficient Bayesian Fine-Tuning using a Reference Model
A recent study on federated learning introduces FedRef, a method that enhances the efficiency of Bayesian fine-tuning using a reference model. This approach not only improves model performance but also prioritizes user data privacy by limiting data sharing. As federated learning becomes increasingly important in AI, especially for applications requiring sensitive data handling, innovations like FedRef are crucial for advancing the field while maintaining ethical standards.
— via World Pulse Now AI Editorial System
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