The rapidly expanding number of Internet of Things (IoT) devices is generating huge quantities of data, but the data privacy and security exposure in IoT devices, especially in the automatic driving system. Federated learning (FL) is a paradigm that addresses data privacy, security, access rights, and access to heterogeneous message issues by integrating a global model based on distributed nodes. However, data poisoning attacks on FL can undermine the benefits, destroying the global model's availability and disrupting model training. To avoid the above issues, we build up a hierarchical defense data poisoning (HDDP) system framework to defend against data poisoning attacks in FL, which monitors each local model of individual nodes via abnormal detection to remove the malicious clients. Whether the poisoning defense server has a trusted test dataset, we design the \underline{l}ocal \underline{m}odel \underline{t}est \underline{v}oting (LMTV) and \underline{k}ullback-\underline{l}eibler divergence \underline{a}nomaly parameters \underline{d}etection (KLAD) algorithms to defend against label-flipping poisoning attacks. Specifically, the trusted test dataset is utilized to obtain the evaluation results for each classification to recognize the malicious clients in LMTV. More importantly, we adopt the kullback leibler divergence to measure the similarity between local models without the trusted test dataset in KLAD. Finally, through extensive evaluations and against the various label-flipping poisoning attacks, LMTV and KLAD algorithms could achieve the $100\%$ and $40\%$ to $85\%$ successful defense ratios under different detection situations.
Defending Against Beta Poisoning Attacks in Machine Learning Models
Department of Computer Engineering Acıbadem University Istanbul, Turkiye（计算机工程系 阿克萨德梅大学 伊斯坦布尔，土耳其）；Department of Computer Engineering Koç University Istanbul, Turkiye（计算机工程系 科大学 伊斯坦布尔，土耳其）
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（北京未来区块链与隐私计算高精尖创新中心）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）
Cordon-MAS: Defending RAG against Knowledge Poisoning via Information-Flow Control
Zhejiang University（浙江大学）；Binjiang Institute of Zhejiang University（浙江大学滨江研究院）；Shanghai Jiao Tong University（上海交通大学）；Zhejiang Lab（浙江实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Hangzhou Dianzi University（杭州电子科技大学）
Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
Nanyang Technological University（南洋理工大学）；Hong Kong University of Science and Technology（香港科技大学）；Jinan University（暨南大学）；Sony Research（索尼研究院）；Zhejiang University（浙江大学）；Beijing Institute of Technology（北京理工大学）
Defending Against Poisoning Attacks in Federated Learning with Blockchain
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Imperial College London（帝国理工学院）；UiT The Arctic University of Norway（挪威北极大学）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
University of Maryland（马里兰大学）；Massachussetts Institute of Technology（麻省理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Berkeley（加利福尼亚大学伯克利分校）
FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
Department of Computer Science Saint Mary's College of California（计算机科学系圣玛丽学院）；Department of Computer Science North Carolina State University（计算机科学系北卡罗来纳州立大学）；Department of Computer Science Boise State University（计算机科学系博伊州立大学）
DP2Guard: A Lightweight and Byzantine-Robust Privacy-Preserving Federated Learning Scheme for Industrial IoT
Southeast University（东南大学）；National University of Singapore（新加坡国立大学）；Huazhong University of Science and Technology（华中科技大学）；Jinan University（暨南大学）；Queensland University of Technology（昆士兰科技大学）；The University of Hong Kong（香港大学）；Nanyang Technological University（南洋理工大学）
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning
University of Alberta（阿尔伯塔大学）；Illinois Institute of Technology（伊利诺伊理工学院）；Michigan State University（密歇根州立大学）
最近对模型训练中的后门攻击的研究表明，污染一小部分训练数据就足以在中毒的测试时间数据上产生不正确的操纵预测，同时在下游任务中保持高的干净精度。后门攻击的隐蔽性给当今的机器学习范式带来了巨大的防御挑战。在本文中，我们探索了通过额外的未标记数据进行自我训练以减轻后门攻击的潜力。我们首先进行了一项试点研究，以表明香草自我训练在后门缓解中无效。受此启发，我们建议通过在自训练伪标签阶段利用强大但适当的数据增强来防御后门攻击。我们发现，新的自我训练机制在很大程度上有助于抵御后门攻击。通过在CIFAR-10和CIFAR-10与额外的未标记的500 K TinyImages数据集的组合上针对不同后门触发器的实验，证明了其有效性。最后，我们探索了将自我监督表示学习与自我训练相结合的方向，以进一步改进后门防御。
SecureDyn-FL: A Robust Privacy-Preserving Federated Learning Framework for Intrusion Detection in IoT Networks
Sir Syed CASE Institute of Technology（Sir Syed CASE 理工学院）；Habib University（哈比布大学）；Yeunganam University（延世南大学）；University of Nottingham Ningbo China（诺丁汉大学宁波分校）；Korea University（高丽大学）
Data Poisoning Attacks on Federated Machine Learning
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Rochester（罗切斯特大学）
