A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
Vertical Federated Learning (VFL) is a federated learning paradigm where multiple
participants, who share the same set of samples but hold different features, jointly train …
participants, who share the same set of samples but hold different features, jointly train …
Passive Inference Attacks on Split Learning via Adversarial Regularization
Split Learning (SL) has emerged as a practical and efficient alternative to traditional
federated learning. While previous attempts to attack SL have often relied on overly strong …
federated learning. While previous attempts to attack SL have often relied on overly strong …
Pistol: Dataset compilation pipeline for structural unlearning of llms
Recently, machine unlearning, which seeks to erase specific data stored in the pre-trained
or fine-tuned models, has emerged as a crucial protective measure for LLMs. However …
or fine-tuned models, has emerged as a crucial protective measure for LLMs. However …
GAN-based data reconstruction attacks in split learning
B Zeng, S Luo, F Yu, G Yang, K Zhao, L Wang - Neural Networks, 2025 - Elsevier
Due to the distinctive distributed privacy-preserving architecture, split learning has found
widespread application in scenarios where computational resources on the client side are …
widespread application in scenarios where computational resources on the client side are …
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection
E Erdoğan, U Tekşen, MS Çeliktenyıldız… - … on Cryptology and …, 2024 - Springer
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 only …
splitting a neural network so that the clients (data holders) compute the first layers and only …