Author

Zhaobin Li

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Open access 2026

Leakage-Guided Selective Threshold Homomorphic Aggregation for Privacy-Preserving Federated Learning

Federated learning (FL) preserves data locality but leaks client properties and samples through shared updates. Homomorphic encryption (HE) hides individual updates from an honest-but-curious server, yet single-key CKKS concentrates trust and full-model encryption inflates bandwidth by an order of magnitude. We propose...

Zhao-Bin Li, Ming-Liang Mo, Jian Shi et al. · IEEE Access · 0 citations

Open access Jul 2026

Privacy-Preserving Federated Learning for Medical Image Classification with Selective Homomorphic Encryption

PASHE-FL offers an empirical privacy–cost trade-off under the stated threat model rather than a formal privacy guarantee, which exploits the structure of personalized federated learning: the client-specific classifier head stays local and is never uploaded, so encryption need only cover the shared backbone.

Zhao-Bin Li, Ming-Liang Mo, Chenchong Du et al. · Applied Informatics · 0 citations