Zobrazeno 1 - 10
of 945
pro vyhledávání: '"To Trong Nghia"'
Autor:
Nguyen, Manh Duong, Nguyen, Trung Thanh, Pham, Huy Hieu, Hoang, Trong Nghia, Nguyen, Phi Le, Huynh, Thanh Trung
Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while storing their data locally. However, a significant ch
Externí odkaz:
http://arxiv.org/abs/2410.03070
Autor:
Nguyen, Minh Hieu, Nguyen, Huu Tien, Nguyen, Trung Thanh, Nguyen, Manh Duong, Hoang, Trong Nghia, Nguyen, Truong Thao, Nguyen, Phi Le
Federated Learning (FL) has emerged as a powerful paradigm for training machine learning models in a decentralized manner, preserving data privacy by keeping local data on clients. However, evaluating the robustness of these models against data pertu
Externí odkaz:
http://arxiv.org/abs/2410.03067
Offline optimization is an emerging problem in many experimental engineering domains including protein, drug or aircraft design, where online experimentation to collect evaluation data is too expensive or dangerous. To avoid that, one has to optimize
Externí odkaz:
http://arxiv.org/abs/2405.05349
Autor:
Sim, Rachael Hwee Ling, Zhang, Yehong, Hoang, Trong Nghia, Xu, Xinyi, Low, Bryan Kian Hsiang, Jaillet, Patrick
Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation. Existing data valuation methods fairly value and reward each party based on shared data or model parameters but neglect th
Externí odkaz:
http://arxiv.org/abs/2404.01676
Randomized smoothing has recently attracted attentions in the field of adversarial robustness to provide provable robustness guarantees on smoothed neural network classifiers. However, existing works show that vanilla randomized smoothing usually doe
Externí odkaz:
http://arxiv.org/abs/2310.07780
Autor:
Pham, Van-Hau, Hoang, Hien Do, Trung, Phan Thanh, Quoc, Van Dinh, To, Trong-Nghia, Duy, Phan The
In order to assess the risks of a network system, it is important to investigate the behaviors of attackers after successful exploitation, which is called post-exploitation. Although there are various efficient tools supporting post-exploitation impl
Externí odkaz:
http://arxiv.org/abs/2309.15518
Autor:
To, Trong-Nghia, Kim, Danh Le, Hien, Do Thi Thu, Khoa, Nghi Hoang, Hoang, Hien Do, Duy, Phan The, Pham, Van-Hau
Recently, there has been a growing focus and interest in applying machine learning (ML) to the field of cybersecurity, particularly in malware detection and prevention. Several research works on malware analysis have been proposed, offering promising
Externí odkaz:
http://arxiv.org/abs/2309.13841
Data summarization is the process of generating interpretable and representative subsets from a dataset. Existing time series summarization approaches often search for recurring subsequences using a set of manually devised similarity functions to sum
Externí odkaz:
http://arxiv.org/abs/2308.13722
Item-to-Item (I2I) recommendation is an important function in most recommendation systems, which generates replacement or complement suggestions for a particular item based on its semantic similarities to other cataloged items. Given that subsets of
Externí odkaz:
http://arxiv.org/abs/2306.03191
Learning an effective global model on private and decentralized datasets has become an increasingly important challenge of machine learning when applied in practice. Existing distributed learning paradigms, such as Federated Learning, enable this via
Externí odkaz:
http://arxiv.org/abs/2306.01240