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pro vyhledávání: '"Cao, XiaoYu"'
Hydrogen-electrical (HE) microgrids are increasingly assuming an important role on the pathway toward decarbonization of energy and transportation systems. This paper studies networked HE microgrids planning (NHEMP), considering a critical but often-
Externí odkaz:
http://arxiv.org/abs/2404.00568
Unscheduled islanding events of microgrids result in the transition between grid-connected and islanded modes and induce a sudden and unknown power imbalance, posing a threat to frequency security. To achieve seamless islanding, we propose a distribu
Externí odkaz:
http://arxiv.org/abs/2401.03381
Video data and algorithms have been driving advances in multi-object tracking (MOT). While existing MOT datasets focus on occlusion and appearance similarity, complex motion patterns are widespread yet overlooked. To address this issue, we introduce
Externí odkaz:
http://arxiv.org/abs/2308.11157
Federated learning is vulnerable to poisoning attacks in which malicious clients poison the global model via sending malicious model updates to the server. Existing defenses focus on preventing a small number of malicious clients from poisoning the g
Externí odkaz:
http://arxiv.org/abs/2210.10936
Due to its distributed nature, federated learning is vulnerable to poisoning attacks, in which malicious clients poison the training process via manipulating their local training data and/or local model updates sent to the cloud server, such that the
Externí odkaz:
http://arxiv.org/abs/2210.00584
Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients corrupt the global model via sending manipulated model updates to the server. Existing defenses mainly rely on Byzantine-robust FL methods, which aim to lear
Externí odkaz:
http://arxiv.org/abs/2207.09209
Motion and interaction of social insects (such as ants) have been studied by many researchers to understand the clustering mechanism. Most studies in the field of ant behavior have only focused on indoor environments, while outdoor environments are s
Externí odkaz:
http://arxiv.org/abs/2204.04380
Autor:
Cao, Xiaoyu, Gong, Neil Zhenqiang
Existing model poisoning attacks to federated learning assume that an attacker has access to a large fraction of compromised genuine clients. However, such assumption is not realistic in production federated learning systems that involve millions of
Externí odkaz:
http://arxiv.org/abs/2203.08669
Autor:
Li, Mengyang, Liu, Hui, He, Baoshan, Xie, Lingling, Cao, Xiaoyu, Jin, Huali, Wei, Min, Ren, Wenjie, Suo, Zhiguang, Xu, Yiwei
Publikováno v:
In Talanta 15 August 2024 276
Autor:
Liu, Hui, Wang, Senyao, He, Baoshan, Xie, Lingling, Cao, Xiaoyu, Wei, Min, Jin, Huali, Ren, Wenjie, Suo, Zhiguang, Xu, Yiwei
Publikováno v:
In Analytica Chimica Acta 8 August 2024 1316