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Full-Text Articles in Information Security
Flgan: Gan-Based Unbiased Federated Learning Under Non-Iid Settings, Zhuoran Ma, Yang Liu, Yinbin Miao, Guowen Xu, Ximeng Liu, Jianfeng Ma, Robert H. Deng
Flgan: Gan-Based Unbiased Federated Learning Under Non-Iid Settings, Zhuoran Ma, Yang Liu, Yinbin Miao, Guowen Xu, Ximeng Liu, Jianfeng Ma, Robert H. Deng
Research Collection School Of Computing and Information Systems
Federated Learning (FL) suffers from low convergence and significant accuracy loss due to local biases caused by non-Independent and Identically Distributed (non-IID) data. To enhance the non-IID FL performance, a straightforward idea is to leverage the Generative Adversarial Network (GAN) to mitigate local biases using synthesized samples. Unfortunately, existing GAN-based solutions have inherent limitations, which do not support non-IID data and even compromise user privacy. To tackle the above issues, we propose a GAN-based unbiased FL scheme, called FlGan, to mitigate local biases using synthesized samples generated by GAN while preserving user-level privacy in the FL setting. Specifically, FlGan first …
When Evolutionary Computation Meets Privacy, Bowen Zhao, Wei-Neng Chen, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Jun Zhang
When Evolutionary Computation Meets Privacy, Bowen Zhao, Wei-Neng Chen, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Jun Zhang
Research Collection School Of Computing and Information Systems
Recently, evolutionary computation (EC) has experienced significant advancements due to the integration of machine learning, distributed computing, and big data technologies. These developments have led to new research avenues in EC, such as distributed EC and surrogate-assisted EC. While these advancements have greatly enhanced the performance and applicability of EC, they have also raised concerns regarding privacy leakages, specifically the disclosure of optimal results and surrogate models. Consequently, the combination of evolutionary computation and privacy protection becomes an increasing necessity. However, a comprehensive exploration of privacy concerns in evolutionary computation is currently lacking, particularly in terms of identifying the object, …
Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao
Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao
Research Collection School Of Computing and Information Systems
In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) …