English(EN) SecureDrive-FL: Joint Differential Privacy and Gradient-Aware Selective Homomorphic Encryption for Federated Driver Monitoring

新框架增强联邦驾驶员监控的隐私性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员开发了SecureDrive-FL，一个用于联邦驾驶员监控的新框架，可增强隐私和安全性。该系统结合了差分隐私随机梯度下降（DP-SGD）与一种新颖的梯度感知选择性同态加密（GASHE）方法。GASHE仅加密超过敏感性阈值的梯度分量，与完全加密相比，降低了计算开销。 AI

影响增强了分布式机器学习中的隐私和安全性，特别适用于驾驶员监控等敏感数据。

排序理由该集群包含一篇详细介绍联邦学习新方法的 ist research paper。 [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Baran Can G\"ul, Hanuma Siddhartha Tunuguntla, Anjana Arvind Naik, Abhishek Vijay Potekar, Nasser Jazdi, Michael Weyrich · 2026-08-28 04:00

SecureDrive-FL：联合差分隐私与梯度感知选择性同态加密用于联邦驾驶员监控

arXiv:2608.27108v1 Announce Type: cross Abstract: Federated Learning (FL) enables privacy-aware distributed training, yet gradient updates remain exploitable: Man-in-the-Middle (MitM) interception exposes updates in transit, while model poisoning corrupts global convergence. We f…