175 citations · 225 across the 16 of their papers we have counts for
8 papers · 1 filter
Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework
Xiaolu Chen, Chenghao Huang, Yanru Zhang +1
The wide adoption of residential photovoltaic (PV) systems introduces new challenges for generation fraud detection (FD). Unlike traditional electricity theft detection, which focu…
Smart Energy Guardian: A Hybrid Deep Learning Model for Detecting Fraudulent PV Generation
Xiaolu Chen, Chenghao Huang, Yanru Zhang +1
With the proliferation of smart grids, smart cities face growing challenges due to cyber-attacks and sophisticated electricity theft behaviors, particularly in residential photovol…
Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity
Xiaolu Chen, Chenghao Huang, Yanru Zhang +1
The rapid expansion of distributed photovoltaic (PV) installations worldwide, many being behind-the-meter systems, has significantly challenged energy management and grid operation…
FedCoSR: Personalized Federated Learning with Contrastive Shareable Representations for Label Heterogeneity in Non-IID Data
Chenghao Huang, Xiaolu Chen, Yanru Zhang +1
Heterogeneity arising from label distribution skew and data scarcity can cause inaccuracy and unfairness in intelligent communication applications that heavily rely on distributed…
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…
DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning
Chenghao Huang, Weilong Chen, Yuxi Chen +2
In federated learning (FL), model aggregation has been widely adopted for data privacy. In recent years, assigning different weights to local models has been used to alleviate the…
Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information
Jinhao Li, Ruichang Zhang, Hao Wang +3
Renewable energy resources (RERs) have been increasingly integrated into large-scale distributed power systems. Considering uncertainties and voltage fluctuation issues introduced…
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…