no code implementations • 27 Mar 2024 • Hanqing Fu, Gaolei Li, Jun Wu, Jianhua Li, Xi Lin, Kai Zhou, Yuchen Liu
Federated neuromorphic learning (FedNL) leverages event-driven spiking neural networks and federated learning frameworks to effectively execute intelligent analysis tasks over amounts of distributed low-power devices but also perform vulnerability to poisoning attacks.
1 code implementation • 15 Mar 2024 • Wanfang Su, Lixing Chen, Yang Bai, Xi Lin, Gaolei Li, Zhe Qu, Pan Zhou
The core philosophy of CMiMC is to preserve discriminative information of individual views in the collaborative view by maximizing mutual information between pre- and post-collaboration features while enhancing the efficacy of collaborative views by minimizing the loss function of downstream tasks.
no code implementations • 7 Mar 2024 • Bingkun Lai, Jiayi He, Jiawen Kang, Gaolei Li, Minrui Xu, Tao Zhang, Shengli Xie
Federated learning is a promising technique for effectively training GAI models in mobile edge networks due to its data distribution.
no code implementations • 18 Jan 2024 • Hang Zhang, Xiang Chen, Rongguang Wang, Renjiu Hu, Dongdong Liu, Gaolei Li
In medical imaging, scans often reveal objects with varied contrasts but consistent internal intensities or textures.
no code implementations • 30 Nov 2023 • Kangkang Sun, Xiaojin Zhang, Xi Lin, Gaolei Li, Jing Wang, Jianhua Li
Researchers have struggled to design fair FL systems that ensure fairness of results.
1 code implementation • 27 Nov 2023 • Xiang Chen, Min Liu, Rongguang Wang, Renjiu Hu, Dongdong Liu, Gaolei Li, Hang Zhang
Medical images are often characterized by their structured anatomical representations and spatially inhomogeneous contrasts.
Ranked #2 on Image Registration on Unpaired-abdomen-CT (using extra training data)
1 code implementation • IEEE Transactions on Network Science and Engineering 2023 • Yilun Hu, Jun Wu, Gaolei Li, Jianhua Li, Jinke Cheng
Extensive experiments based on multiple benchmark datasets like CICIDS2017 and DAPT 2020 prove the superiority of proposed PFTD.
no code implementations • 2 Aug 2023 • Xing Ai, Jialong Zhou, Yulin Zhu, Gaolei Li, Tomasz P. Michalak, Xiapu Luo, Kai Zhou
Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry.
no code implementations • 13 Jun 2023 • Gaolei Li, YuanYuan Zhao, Wenqi Wei, Yuchen Liu
Secondly, to rearm current security strategies, an finetuning-based deployment mechanism is proposed to transfer learned knowledge into the student model, while minimizing the defense cost.
no code implementations • 13 Jun 2023 • Haochen Mei, Gaolei Li, Jun Wu, Longfei Zheng
In this paper, we propose a novel privacy inference-empowered stealthy backdoor attack (PI-SBA) scheme for FL under non-IID scenarios.
no code implementations • 1 Feb 2023 • Gaolei Li, YuanYuan Zhao, Yi Li
Most existing studies on trustworthy FL aim to eliminate data poisoning threats that are produced by malicious clients, but in many cases, eliminating model poisoning attacks brought by fake servers is also an important objective.
no code implementations • 25 Oct 2022 • Yulin Zhu, Liang Tong, Gaolei Li, Xiapu Luo, Kai Zhou
Graph Neural Networks (GNNs) are vulnerable to data poisoning attacks, which will generate a poisoned graph as the input to the GNN models.
no code implementations • 15 Mar 2021 • Ge Ren, Jun Wu, Gaolei Li, Shenghong Li
The smartphone and laptop can be unlocked by face or fingerprint recognition, while neural networks which confront numerous requests every day have little capability to distinguish between untrustworthy and credible users.