2 citations · 4 across the 9 of their papers we have counts for
3 papers · 1 filter
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…
Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI
Santhosh Parampottupadam, Melih Coşğun, Sarthak Pati +7
Background: Federated learning (FL) enables collaborative training of clinical AI models without centralizing patient data, but adoption is limited by privacy concerns, heterogeneo…