Zobrazeno 1 - 10
of 832
pro vyhledávání: '"Pan, Miao"'
This paper addresses a distributed leader-follower formation control problem for a group of agents, each using a body-fixed camera with a limited field of view (FOV) for state estimation. The main challenge arises from the need to coordinate the agen
Externí odkaz:
http://arxiv.org/abs/2409.11394
Training latency is critical for the success of numerous intrigued applications ignited by federated learning (FL) over heterogeneous mobile devices. By revolutionarily overlapping local gradient transmission with continuous local computing, FL can r
Externí odkaz:
http://arxiv.org/abs/2407.00943
Autor:
Su, Huai-an, Geng, Jiaxiang, Li, Liang, Qin, Xiaoqi, Hou, Yanzhao, Wang, Hao, Fu, Xin, Pan, Miao
As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices' computing and communication heterogeneity. Some pioneering r
Externí odkaz:
http://arxiv.org/abs/2405.00885
While quantum computing has strong potential in data-driven fields, the privacy issue of sensitive or valuable information involved in the quantum algorithm should be considered. Differential privacy (DP), which is a fundamental privacy tool widely u
Externí odkaz:
http://arxiv.org/abs/2403.09173
We study federated unlearning, a novel problem to eliminate the impact of specific clients or data points on the global model learned via federated learning (FL). This problem is driven by the right to be forgotten and the privacy challenges in FL. W
Externí odkaz:
http://arxiv.org/abs/2401.11018
Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. To protect quantum computing privacy, pioneering research efforts proposed to redefine differential privacy (DP) in quantum computing, i.e., quantum
Externí odkaz:
http://arxiv.org/abs/2312.14521
Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, data leakage in quantum machine learning (QML) may present privacy risks
Externí odkaz:
http://arxiv.org/abs/2312.11126
Quantum computing has been widely applied in various fields, such as quantum physics simulations, quantum machine learning, and big data analysis. However, in the domains of data-driven paradigm, how to ensure the privacy of the database is becoming
Externí odkaz:
http://arxiv.org/abs/2312.08210
The Metaverse is a virtual world, an immersive experience, a new human-computer interaction, built upon various advanced technologies. How to protect Metaverse personal information and virtual properties is also facing new challenges, such as new att
Externí odkaz:
http://arxiv.org/abs/2310.03162
Recent advances in machine learning and natural language processing have fostered the enormous prosperity of smart voice assistants and their services, e.g., Alexa, Google Home, Siri, etc. However, voice spoofing attacks are deemed to be one of the m
Externí odkaz:
http://arxiv.org/abs/2309.15203