An efficient approach for privacy preserving decentralized deep learning models based on secure multi-party computation

Đánh giá bài viết

Xem thêm

02/08/2026. A Comprehensive Survey and Taxonomy on Privacy-Preserving Deep Learning

02/08/2026. Privacy-preserving Deep Learning Model with Integer Quantization and Secure Multi-party Computation

15/07/2026. A lower bound for the induced matching number of a class of graphs due to Grimaldi and the Castelnuovo–Mumford regularity

02/08/2026. PriFLRC: A secure multi-party computation-based privacy-enhanced federated learning scheme resilient to collusion

02/08/2026. A Blockchain-Based Chain of Custody for Digital Evidence: Design and Evaluation

02/08/2026. Amplified Gradient Inversion Attacks on Federated Learning Frameworks

fami

Văn phòng: Số 1 Đại Cồ Việt, phường Bạch Mai, Thành phố Hà Nội
Điện thoại: 04 3869 2137 - Fax: 04 3868 2470
Email: fami@hust.edu.vn
Website: https://fami.hust.edu.vn