Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Thai, My T."'
With the rapid adoption of Federated Learning (FL) as the training and tuning protocol for applications utilizing Large Language Models (LLMs), recent research highlights the need for significant modifications to FL to accommodate the large-scale of
Externí odkaz:
http://arxiv.org/abs/2403.04784
Federated Learning (FL) has garnered significant attention for its potential to protect user privacy while enhancing model training efficiency. For that reason, FL has found its use in various domains, from healthcare to industrial engineering, espec
Externí odkaz:
http://arxiv.org/abs/2311.13739
This paper introduces FairDP, a novel mechanism designed to achieve certified fairness with differential privacy (DP). FairDP independently trains models for distinct individual groups, using group-specific clipping terms to assess and bound the disp
Externí odkaz:
http://arxiv.org/abs/2305.16474
Publikováno v:
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:5714-5730, 2023
Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection through a coordinating server. In this paper, we propose a new active membership inference (AMI) attack carried out b
Externí odkaz:
http://arxiv.org/abs/2302.12685
Recent development in the field of explainable artificial intelligence (XAI) has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in which an explanation is provided together with the model prediction in response to each query.
Externí odkaz:
http://arxiv.org/abs/2212.04454
In this paper, we show that the process of continually learning new tasks and memorizing previous tasks introduces unknown privacy risks and challenges to bound the privacy loss. Based upon this, we introduce a formal definition of Lifelong DP, in wh
Externí odkaz:
http://arxiv.org/abs/2207.12831
Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Conseque
Externí odkaz:
http://arxiv.org/abs/2205.05611
Autor:
Nguyen, Truc, Thai, My T.
Publikováno v:
IEEE/ACM Transactions on Networking, vol. 32, no. 1, pp. 833-843, Feb. 2024
Federated learning is known to be vulnerable to both security and privacy issues. Existing research has focused either on preventing poisoning attacks from users or on concealing the local model updates from the server, but not both. However, integra
Externí odkaz:
http://arxiv.org/abs/2202.03402
In this paper, we focus on preserving differential privacy (DP) in continual learning (CL), in which we train ML models to learn a sequence of new tasks while memorizing previous tasks. We first introduce a notion of continual adjacent databases to b
Externí odkaz:
http://arxiv.org/abs/2110.05223
Autor:
Nguyen, Truc, Thai, My T.
Publikováno v:
IEEE Transactions on Computers, vol. 72, no. 3, pp. 641-652, 1 March 2023
Since 2016, sharding has become an auspicious solution to tackle the scalability issue in legacy blockchain systems. Despite its potential to strongly boost the blockchain throughput, sharding comes with its own security issues. To ease the process o
Externí odkaz:
http://arxiv.org/abs/2007.08600