Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning
Elmore Family School of Electrical and Computer Engineering, Purdue University（电子工程学院，普渡大学）；Department of Electrical and Computer Engineering, University of Akron（电气与计算机工程系，阿克隆大学）；Department of Electrical Engineering, University at Buffalo-SUNY（电气工程系，布法罗大学）
Seohyun Lee, Wenzhi Fang, Anindya Bijoy Das, Seyyedali Hosseinalipour, David J. Love, Christopher G. Brinton
备注：
This paper is currently under review in the IEEE/ACM Transactions on Networking Special Issue on AI and Networking
Federated learning (FL) is vulnerable to backdoor attacks, where adversaries alter model behavior on target classification labels by embedding triggers into data samples. While these attacks have received considerable attention in horizontal FL, they are less understood for vertical FL (VFL), where devices hold different features of the samples, and only the server holds the labels. In this work, we propose a novel backdoor attack on VFL which (i) does not rely on gradient information from the server and (ii) considers potential collusion among multiple adversaries for sample selection and trigger embedding. Our label inference model augments variational autoencoders with metric learning, which adversaries can train locally. A consensus process over the adversary graph topology determines which datapoints to poison. We further propose methods for trigger splitting across the adversaries, with an intensity-based implantation scheme skewing the server towards the trigger. Our convergence analysis reveals the impact of backdoor perturbations on VFL indicated by a stationarity gap for the trained model, which we verify empirically as well. We conduct experiments comparing our attack with recent backdoor VFL approaches, finding that ours obtains significantly higher success rates for the same main task performance despite not using server information. Additionally, our results verify the impact of collusion on attack performance.
Exploiting Layer-Specific Vulnerabilities to Backdoor Attack in Federated Learning
School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran（塔里斯坦大学电气与计算机工程学院）；School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran（谢尔兹大学电气与计算机工程学院）；Okinawa Institute of Science and Technology, Okinawa, Japan（冲绳科学技术研究院）；School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran（基础科学研究院（IPM）计算机科学学院）
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning
University of South Florida（南佛罗里达大学）；University of Massachusetts, Lowell（马萨诸塞大学洛厄尔分校）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）；University of Kentucky（肯塔基大学）
Neurotoxin: Durable Backdoors in Federated Learning
Southeast University（东南大学）；Princeton University（普林斯顿大学）；University of California at Berkeley（加州大学伯克利分校）；International Computer Science Institute（国际计算机科学研究所）
由于其分散的性质，联邦学习（FL）系统在训练过程中对对抗性后门攻击具有固有的脆弱性。在这种类型的攻击中，攻击者的目标是使用中毒更新将所谓的后门植入到学习模型中，以便在测试时，模型的输出可以固定为特定输入的给定目标。(As一个简单的玩具示例，如果用户将“people from New York”键入到使用后门下一单词预测模型的移动键盘应用中，则该模型可以将句子自动完成为“people from New York are rude”）。先前的工作已经表明，后门可以插入到FL模型中，但是这些后门通常不耐用，即，在攻击者停止上载中毒更新之后，它们不会保留在模型中。因此，由于训练通常在生产FL系统中逐步继续，因此插入的后门可能无法存活直到部署。在这里，我们提出了神经毒素，一个简单的一行修改现有的后门攻击，通过攻击参数，在训练过程中变化较小的幅度。我们对十个自然语言处理和计算机视觉任务进行了详尽的评估，我们发现我们可以将最先进的后门的耐久性提高一倍。
Fully Decentralized Cooperative Multi-Agent Reinforcement Learning is A Context Modeling Problem
School of Computer Science, Nanjing University of Posts and Telecommunications（南京邮电大学计算机科学学院）；School of Intelligent Science and Technology, Nanjing University（南京大学智能科学与技术学院）；School of Intelligent Science（南京大学智能科学学院）
TDC-Cache: A Trustworthy Decentralized Cooperative Caching Framework for Web3.0
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究中心）；Purple Mountain Laboratories（紫金山实验室）；School of Electrical Engineering and Telecommunications, The University of New South Wales（新南威尔士大学电子与电信工程学院）
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；East China Normal University（华东师范大学）；Shanghai Jiao Tong University（上海交通大学）
Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework
Washington University in St. Louis（华盛顿大学圣路易斯分校）；Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
POLAR: Policy-based Layerwise Reinforcement Learning Method for Stealthy Backdoor Attacks in Federated Learning
Columbia University（哥伦比亚大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai University of Electric Power（上海电力大学）；National University of Singapore（新加坡国立大学）；Stevens Institute of Technology（斯蒂文斯理工学院）；Queen’s University Belfast（贝尔法斯特女王大学）
Structured Cooperative Learning with Graphical Model Priors
University of Science and Technology of China（中国科学技术大学）；University of Maryland, College Park（马里兰大学帕克分校）；The University of Sydney（悉尼大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
