Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Zhang, Lefeng"'
Model extraction attacks currently pose a non-negligible threat to the security and privacy of deep learning models. By querying the model with a small dataset and usingthe query results as the ground-truth labels, an adversary can steal a piracy mod
Externí odkaz:
http://arxiv.org/abs/2407.01251
Federated learning is a promising privacy-preserving paradigm for distributed machine learning. In this context, there is sometimes a need for a specialized process called machine unlearning, which is required when the effect of some specific trainin
Externí odkaz:
http://arxiv.org/abs/2406.12516
Machine unlearning is an emerging technology that has come to attract widespread attention. A number of factors, including regulations and laws, privacy, and usability concerns, have resulted in this need to allow a trained model to forget some of it
Externí odkaz:
http://arxiv.org/abs/2406.10954
Machine unlearning enables pre-trained models to eliminate the effects of partial training samples. Previous research has mainly focused on proposing efficient unlearning strategies. However, the verification of machine unlearning, or in other words,
Externí odkaz:
http://arxiv.org/abs/2406.10953
The development of Large Language Models (LLMs) faces a significant challenge: the exhausting of publicly available fresh data. This is because training a LLM needs a large demanding of new data. Federated learning emerges as a promising solution, en
Externí odkaz:
http://arxiv.org/abs/2406.04076
With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. Traditional unlearning methods, however, often lack verifiabl
Externí odkaz:
http://arxiv.org/abs/2405.20776
Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more. Yet a special need h
Externí odkaz:
http://arxiv.org/abs/2306.03558
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Kang, Huiwen, Huang, Danyang, Jing, Jiaru, Zhang, Wei, Zhang, Lei, Wang, Jingyu, Liu, Ziyan, Han, Lin, Wang, Ziyan, Zhang, Lefeng, Gao, Ai
Publikováno v:
Nano Research; Aug2024, Vol. 17 Issue 8, p7365-7375, 11p
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.