8 papers · 1 filter
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…
Bridging Local and Federated Data Normalization in Federated Learning: A Privacy-Preserving Approach
Melih Coşğun, Mert Gençtürk, Sinem Sav
Data normalization is a crucial preprocessing step for enhancing model performance and training stability. In federated learning (FL), where data remains distributed across multipl…
A Taxonomy of Attacks and Defenses in Split Learning
Aqsa Shabbir, Halil İbrahim Kanpak, Alptekin Küpçü +1
Split Learning (SL) has emerged as a promising paradigm for distributed deep learning, allowing resource-constrained clients to offload portions of their model computation to serve…
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…