5 papers
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning
Emre Ozfatura, Kerem Ozfatura, Baturalp Buyukates +3
In federated learning (FL), profiling and verifying each client is inherently difficult, which introduces a significant security vulnerability: malicious clients, commonly referred…
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…
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…
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection
Ege Erdogan, Unat Teksen, Mehmet Salih Celiktenyildiz +2
Split learning enables efficient and privacy-aware training of a deep neural network by splitting a neural network so that the clients (data holders) compute the first layers and o…
Gamu Blue: A Practical Tool for Game Theory Security Equilibria
Ameer Taweel, Burcu Yıldız, Alptekin Küpçü
The application of game theory in cybersecurity enables strategic analysis, adversarial modeling, and optimal decision-making to address security threats' complex and dynamic natur…