175 citations · 181 across the 4 of their papers we have counts for
4 papers
FedTADBench: Federated Time-Series Anomaly Detection Benchmark
Fanxing Liu, Cheng Zeng, Le Zhang +5
Time series anomaly detection strives to uncover potential abnormal behaviors and patterns from temporal data, and has fundamental significance in diverse application scenarios. Co…
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets
Chenghao Huang, Weilong Chen, Shengrong Bu +1
Short-term load forecasting (STLF) plays a significant role in the operation of electricity trading markets. Considering the growing concern of data privacy, federated learning (FL…
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
Yingjie Zhou, Xucheng Song, Yanru Zhang +3
Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks…
Deep Anomaly Detection in Packet Payload
Jiaxin Liu, Xucheng Song, Yingjie Zhou +4
With the widespread adoption of cloud services, especially the extensive deployment of plenty of Web applications, it is important and challenging to detect anomalies from the pack…