arXiv:2607. 24556v1 Announce Type: cross Abstract: Split learning enables collaborative model training by partitioning neural networks across clients and servers.
By Akarsh K. Nair, Muhammad Arifur Rahman, David Brown, Mufti Mahmud
arXiv:2608. 13773v1 Announce Type: cross Abstract: Neural networks are increasingly deployed in high-stakes applications with growing privacy leakage concerns.
By Leonardo Magliolo, Vito Paolo Pastore, Giuseppe Valenzise, Enzo Tartaglione
arXiv:2412. 12640v2 Announce Type: replace Abstract: The increasing demand for data privacy, alongside the benefits of aggregating data from networked devices, has catalyzed the emergence of federated learning (FL).
By Rui Zhang, Ka-Ho Chow
The paper "Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses" provides a comprehensive review of model inversion (MI) attacks that exploit trained deep neural networks to reconstruct private training data. It traces the evolution of MI from early machine‑learning contexts to recent DNN‑based attacks across various modalities and learning tasks, offering a detailed taxonomy and comparative analysis of both attacks and defenses. The authors also present an open‑source toolbox on GitHub to support further research in this area.
By Hao Fang, Yixiang Qiu, Hongyao Yu, Wenbo Yu, Jiawei Kong, Baoli Chong, Bin Chen, Xuan Wang, Shu-Tao Xia, Ke Xu
arXiv:2512. 12840v2 Announce Type: replace-cross Abstract: Vertical Federated Learning (VFL) enables collaborative model training across organizations that share common user samples but hold disjoint feature spaces.
By Sindhuja Madabushi, Haider Ali, Ahmad Faraz Khan, Rui Ning, Hongyi Wu, Chunsheng Xin, Ali. R. Butt, Jin-Hee Cho
arXiv:2606. 14210v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in privacy-sensitive domains, where users must balance the risk of data exposure through external APIs against the high computational cost of local deployment.
By Zixuan Gu, Xiaojun Ye, Yang Liu