2 citations · 4 across the 9 of their papers we have counts for
3 papers · 1 filter
Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data
Atilla Akkus, Masoud Poorghaffar Aghdam, Mingjie Li +4
Large language models (LLMs) have demonstrated significant success in various domain-specific tasks, with their performance often improving substantially after fine-tuning. However…
CURE: Privacy-Preserving Split Learning Done Right
Halil Ibrahim Kanpak, Aqsa Shabbir, Esra Genç +2
Training deep neural networks often needs large datasets stored and processed in the cloud, and in sensitive fields like healthcare, these workflows must follow strict privacy rule…
How to Privately Tune Hyperparameters in Federated Learning? Insights from a Benchmark Study
Natalija Mitic, Apostolos Pyrgelis, Sinem Sav
In this paper, we address the problem of privacy-preserving hyperparameter (HP) tuning for cross-silo federated learning (FL). We first perform a comprehensive measurement study th…