Lele Zheng

Achieving a 12.31% accuracy boost in open-set WiFi RF fingerprinting, C$^2$T-OpenMax redefines class representation geometry for better device authentication.

Noise-aware shrinkage can enhance the utility of differentially private fine-tuning, outperforming traditional methods without compromising privacy or efficiency.

Geometry-consistent aggregation can boost federated learning accuracy by over 2% while maintaining differential privacy.