The emergence of Large Language Models (LLMs) has necessitated the adoption of distributed training techniques, involving the deployment of thousands of GPUs to train a single model. Unfortunately, the efficiency of large-scale distributed training systems is often suboptimal due to the increased likelihood of hardware errors in high-end GPU products and the heightened risk of network traffic collisions. Moreover, any local hardware failure can disrupt training tasks, and the inability to swiftly identify faulty components leads to a significant waste of GPU resources. And, prolonged communication due to traffic collisions can substantially increase GPU waiting times. To address these challenges, we propose a communication-driven solution, namely the C4. The key insights of C4 are twofold. First, the load in distributed training exhibits homogeneous characteristics and is divided into iterations through periodic synchronization, therefore hardware anomalies would incur certain syndrome in collective communication. By leveraging this feature, C4 can rapidly identify the faulty components, swiftly isolate the anomaly, and restart the task, thereby avoiding resource wastage caused by delays in anomaly detection. Second, the predictable communication model of collective communication, involving a limited number of long-lived flows, allows C4 to efficiently execute traffic planning, substantially reducing bandwidth competition among these flows. The C4 has been extensively deployed across real-world production systems in a hyperscale cloud provider, yielding a significant improvement in system efficiency, from 30% to 45%. This enhancement is attributed to a 30% reduction in error-induced overhead and a 15% reduction in communication costs.
Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning
LG CNS；Department of Industrial & Management Engineering, Korea University（高丽大学产业与管理工程系）；Department of Industrial Engineering, Seoul National University（首尔大学工业工程系）
Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series
Department of Computer Engineering（计算机工程系）；Polytechnique Montreal（蒙特利尔理工学院）；Department of Compute Engineering（计算机工程系）；Department of Computer Science（计算机科学系）；Université de Sherbrooke（谢布鲁克大学）
Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT
Department of Electronic Systems, Norwegian University of Science and Technology（电子系统系，挪威科学与技术大学）；Department of Engineering Cybernetics, Norwegian University of Science and Technology（工程控制系，挪威科学与技术大学）；Department of Gas Technology, SINTEF Energy Research（气体技术系，SINTEF能源研究）
LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；CUHK-Shenzhen（香港中文大学（深圳））；Department of DSAI, Monash University（莫纳什大学数据科学与人工智能系）；KAUST（阿卜杜拉国王科技大学）；International Center of Future Science, Jilin University（吉林大学未来科学国际合作中心）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China（教育部知识驱动人机智能工程研究中心）
Enhancing ECG Classification Robustness with Lightweight Unsupervised Anomaly Detection Filters
NXP Semiconductors（NXP半导体公司）；Institute of Medical Technology and Intelligent Systems（医学技术与智能系统研究所）；Faculty of Computer Science and Digital Society（计算机科学与数字社会学院）
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）
Iterative Encoding-Decoding VAEs Anomaly Detection in NOAA's DART Time Series: A Machine Learning Approach for Enhancing Data Integrity for NASA's GRACE-FO Verification and Validation
University of California at Los Angeles（加利福尼亚大学洛杉矶分校）；NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）
Foundation Models for Anomaly Detection: Vision and Challenges
School of Computing Technologies, RMIT University（计算技术学院，皇家墨尔本理工大学）；STEM, the University of South Australia（STEM，南澳大利亚大学）；School of Computing and Information Systems, University of Melbourne（计算与信息学院，墨尔本大学）；CSIRO's Data61（CSIRO的数据61）
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
Heilongjiang University（黑龙江大学）；École de technologie supérieure, Université du Québec（魁北克大学高等技术学院）；Nanyang Technological University（南洋理工大学）；Wuhan University of Technology（武汉理工大学）；Alibaba-NTU Joint Research Institute（阿里巴巴-南洋理工大学联合研究院）
Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
College of Information Technology, United Arab Emirates University, Al Ain, Abu Dhabi, UAE（信息科技学院，阿拉伯联合酋长国大学，阿因，阿布扎比，阿联酋）；College of Computing and Informatics, University of Sharjah, Sharjah, UAE（计算与信息学院，谢尔杰大学，谢尔杰，阿联酋）；College of Technological Innovation, Zayed University, Abu Dhabi.UAE（技术创新学院，扎耶德大学，阿布扎比，阿联酋）
