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Jun, 2024
无破坏:隐私保护和拜占庭强健的联邦学习
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning
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Zhibo Xing, Zijian Zhang, Zi'ang Zhang, Jiamou Liu, Liehuang Zhu...
TL;DR
本文旨在构建一个隐私保护、拜占庭强健的联邦学习方案,提供一个无破坏者环境,以保护数据隐私并排除恶意攻击者。我们通过构建模型过滤器、采用零知识证明提供进一步的隐私保护、采用秘密共享提供可验证的安全聚合,证明了NoV能有效应对包括PGD在内的数据和模型污染攻击,并超越其他相关方案。
Abstract
federated learning
allows several clients to train one machine learning model jointly without sharing private data, providing privacy protection. However, traditional
federated learning
is vulnerable to
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