VPDR improves the privacy-utility trade-off in ProtoPFL by allocating less noise to high-variance discriminative prototype dimensions via VPP and using DCR to keep feature norms near the clipping threshold without harming predictions.
Fair feder- ated learning under domain skew with local consistency and domain diversity
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Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning
VPDR improves the privacy-utility trade-off in ProtoPFL by allocating less noise to high-variance discriminative prototype dimensions via VPP and using DCR to keep feature norms near the clipping threshold without harming predictions.