2 citations · 4 across the 9 of their papers we have counts for
3 papers · 1 filter
HE-OFT: Privacy-Preserving One-Shot Federated Fine-Tuning under Homomorphic Encryption
Halil İbrahim Kanpak, Sinem Sav, Alptekin Küpçü
Many organizations adapt large pretrained models to their own tasks by fine-tuning on private data. Several of these parties often hold data for the same task and wish to fine-tune…
Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks
Santhosh Parampottupadam, Andres Martinez, Dimitrios Bounias +3
Federated learning (FL) enables multi-institutional training on clinical text without sharing raw data, but gradient inversion can reconstruct sensitive information from shared mod…
HADES: Privacy-Preserving Federated Learning via Selective Feature Encryption and Hybrid Model Fusion
Ergün Batuhan Kaynak, Kerem Bayramoglu, Sinem Sav
In this paper, we address the challenge of privacy-preserving training in federated learning (FL) by introducing a novel framework that selectively encrypts only the most privacy-s…