Gradient Inversion Attacks: Impact Factors Analyses and Privacy Enhancement

Gradient inversion attacks (GIAs) have posed significant challenges to the emerging paradigm of distributed learning, which aims to reconstruct the private training data of clients (participating parties in distributed training) through the shared parameters. For counteracting GIAs, a large number o...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2024-12, Vol.46 (12), p.9834-9850
Hauptverfasser:	Ye, Zipeng, Luo, Wenjian, Zhou, Qi, Zhu, Zhenqian, Shi, Yuhui, Jia, Yan
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial intelligence security Computational modeling Data models Data privacy distributed learning gradient inversion attacks (GIAs) Mathematical models Privacy privacy protection Training Training data
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