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Yuanzhe Peng

2 accepted papers

2026

Equilibrium-Driven Vertical Federated Learning with Selective Privacy Protection

AAAI 2026technical

Vertical Federated Learning (VFL) enables multiple clients with feature-partitioned data to collaboratively train models while preserving privacy by transmitting embeddings instead of raw data. However, such embeddings can still expose sensitive attributes (e.g., gender or race) unrelated to the tar

Cited by 0SourcePDFScholar
2024

Fedmm: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology

ICASSP 2024accepted

The fusion of complementary multimodal information is crucial in computational pathology for accurate diagnostics. However, existing multimodal learning approaches necessitate access to users’ raw data, posing substantial privacy risks. While Federated Learning (FL) serves as a privacy-preserving al…

Cited by 0SourceScholar
Yuanzhe Peng — accepted AI-conference papers · AIConfPaper