Split learning (SL) is a privacy-preserving distributed deep learning method used to train a collaborative model without the need for sharing of patient's raw data between clients. In split learning, an additional privacy-preserving algorithm called no-peek algorithm can be incorporated, which is robust to adversarial attacks. The privacy benefits offered by split learning make it suitable for practice in the healthcare domain. However, the split learning algorithm is flawed as the collaborative model is trained sequentially, i.e., one client trains after the other. We point out that the model trained using the split learning algorithm gets biased towards the data of the clients used for training towards the end of a round. This makes SL algorithms highly susceptible to the order in which clients are considered for training. We demonstrate that the model trained using the data of all clients does not perform well on the client's data which was considered earliest in a round for training the model. Moreover, we show that this effect becomes more prominent with the increase in the number of clients. We also demonstrate that the SplitFedv3 algorithm mitigates this problem while still leveraging the privacy benefits provided by split learning.
VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle Robustness
State Key Laboratory of Intelligent Transportation Systems（智能交通系统国家重点实验室）；School of Transportation Science and Technology, Beihang University（北京航空航天大学交通科学与工程学院）；Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute（伦斯勒理工学院土木与环境工程系）
To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Huawei International, Singapore（华为国际（新加坡））；State University of New York at Buffalo（纽约州立大学布法罗分校）
JASPER: Special Session on Joint Reliability And Security Assessment of SPlit Computing for Edge Robustness
Politecnico di Torino（都灵理工大学）；Tallinn University of Technology（塔林理工大学）；University of California, Irvine（加州大学欧文分校）；Brandenburg University of Technology（勃兰登堡工业大学）；Humboldt University of Berlin（柏林洪堡大学）
Geometric origin of adversarial vulnerability in deep learning
Anhui Provincial Key Laboratory of Low-Energy Quantum Materials and Devices（安徽省低能量子材料与器件重点实验室）；High Magnetic Field Laboratory, HFIPS, Chinese Academy of Sciences（高磁场实验室，HFIPS，中国科学院）；University of Science and Technology of China（中国科学技术大学）；PMI Lab, School of Physics, Sun Yat-sen University（PMI实验室，物理学院，中山大学）；Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices（广东省磁电物理与器件重点实验室）
You Only Train Once: A Flexible Training Framework for Code Vulnerability Detection Driven by Vul-Vector
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；China University of Mining and Technology, Beijing（中国矿业大学（北京））；Hong Kong University of Science and Technology (Guangzhou), Deep Interdisciplinary Intelligence Lab（香港科技大学（广州）深度跨学科智能实验室）；Hong Kong University of Science and Technology (Guangzhou), Deep Interdisciplinary Intelligence Lab, Institute of Deep Perception Technology, JITRI（香港科技大学（广州）深度跨学科智能实验室，感知技术研究所，JITRI）
Batch Normalization is a Cause of Adversarial Vulnerability
University of Guelph（圭尔夫大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；University of Waterloo（滑铁卢大学）；Perimeter Institute for Theoretical Physics（圆周理论物理研究所）；University College London（伦敦大学学院）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Google Brain（谷歌大脑）
Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training
Chongqing Key Laboratory of Computational Intelligence（重庆计算智能重点实验室）；Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education（教育部网络空间大数据智能安全重点实验室）；Key Laboratory of Big Data Intelligent Computing（大数据智能计算重点实验室）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络空间安全学院）；School of Computer Science and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）
Enhancing Continual Learning for Software Vulnerability Prediction: Addressing Catastrophic Forgetting via Hybrid-Confidence-Aware Selective Replay for Temporal LLM Fine-Tuning
School of Computer Science, University of Nottingham, Nottingham, United Kingdom（诺丁汉大学计算机科学学院）；School of Informatics and Computing, Northern Arizona University, Flagstaff, United States of America（北亚利桑那大学信息学院）；School of Computer Science, University of Nottingham, Ningbo, China（诺丁汉大学宁波校区）
Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI
department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University（放射医学与核医学科，宣武医院，首都医科大学）；Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics（北京磁共振成像与脑信息学重点实验室）；Beijing United Imaging Research Institute of Intelligent Imaging（北京智能成像联合研究院）
