Author

Ali Burak Ünal

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#federated learning Preprint Oct 2026

FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains

FedCKA is proposed, a Centered Kernel Alignment (CKA)-based strategy that dynamically handles the personalization-globalization trade-off, and outperforms established federated baselines, including FedBN, FedRep, and FedSelect, improving average NDS by 7 percentage points over the strongest baseline.

Jolle Verhoog, Ali Burak Ünal, Holger Caesar · 0 citations

#machine learning Preprint Aug 2026

Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation

This work proposes a secure multi-party computation framework that enables the training and inference of CellCnn entirely on secret-shared data, and preserves accuracy close to its plaintext counterpart while outperforming the prior privacy-preserving baseline.

S. S. Magara, Esther Havemann, Debora Jutz et al. · 0 citations