From the 2 of 6 linked papers with an AI index.
6 papers
Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks
Santhosh Parampottupadam, Andres Martinez, Dimitrios Bounias +3
The paper measures how much patient information can be reconstructed from model gradients in federated learning of radiology reports, comparing three different tokenizers and showi…
Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI
Santhosh Parampottupadam, Melih CoÅÄun, Sarthak Pati +7
The paper proposes a federated learning framework that adjusts differential privacy noise based on each healthcare institution's compliance level, allowing lower‑compliance sites t…
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 CoÅÄun, 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…