Resilience of Federated Learning Against False Data Injection Attacks in Energy Forecasting

Shabbir, Attia; Manzoor, Habib Ullah; Ahmed, Ridha Alaa; Halim, Zahid

doi:10.1109/gecost60902.2024.10475064

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Furthermore, FL can leverage data from UAVs equipped with sensors to automatically identify the presence of defective parts in overhead power distribution systems [39]. Moreover, FL is more energy and communication efficient while being more robust agianst adversarial attacks [40][41][42].…”

Section: Scopementioning

confidence: 99%

Machine learning‐assisted anomaly detection for power line components: A case study in Pakistan

Basit,

Manzoor,

Akram

et al. 2024

The Journal of Engineering

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A continuous supply of electricity is necessary to maintain an acceptable standard of life, and the power distribution system's overhead line components play a crucial role in this matter. In Pakistan, identifying defective parts often necessitates human involvement. An unmanned aerial vehicle was used to gather a collection of 10,343 photos to automate this procedure. Using supervised and unsupervised machine learning methods, a number of automated anomaly detection systems were created. Support vector machine, random forest, VGG16, and ResNet50 were used as supervised machine learning models, and a convolutional auto‐encoder was used as the unsupervised machine learning model. VGG16 achieved the best accuracy of 99.00% while random forest achieved the worst accuracy of 72.49%. The convolutional auto‐encoder was successful in distinguishing between normal and abnormal components. The aforementioned machine learning models can be put on unmanned aerial vehicles to immediately identify defective parts.

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Section: Scopementioning

confidence: 99%

Machine learning‐assisted anomaly detection for power line components: A case study in Pakistan

Basit,

Manzoor,

Akram

et al. 2024

The Journal of Engineering

Self Cite

View full text Add to dashboard Cite

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Swarm‐Optimized ZnO/CdS/CIGS/GaAs Solar Cell for Enhanced Efficiency and Thermal Resilience

Manzoor,

Hussain

et al. 2024

Adv Energy and Sustain Res

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Optimizing solar cell design is vital for boosting efficiency, cutting production costs, and meeting the increasing demand for renewable energy solutions. Through meticulous adjustments in material compositions and device architectures, optimization enhances energy conversion efficiency, making solar power more competitive and adaptable across various applications. This article presents the optimization and efficiency enhancement of a ZnO/CdS/CIGS solar cell with GaAs. The optimization process utilizes the particle swarm optimization algorithm with a step‐by‐step approach. Solar cells are designed using SCAPS‐1D software, and optimization is performed using Python. The optimized ZnO/CdS/CIGS solar cell achieves an efficiency of 32.4%, which rises to 44.7% upon integrating a GaAs layer. Further efficiency gains are observed, reaching 53.2% through back contact optimization, providing a power density of 54 mW cm−2. Optimization also notices a significant improvement in quantum efficiency. The cells are tested under concentrated solar irradiance (1000–10 000 W m−2) and temperatures (300–800 K). Results show that at 10 000 W m−2 and 800 K, the ZnO/CdS/CIGS/GaAs cell requires 53.9% less material than the ZnO/CdS/CIGS cell. Thus, adding GaAs enhances efficiency and thermal resilience, making it ideal for concentrated photovoltaics.

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Robustness Against Data Integrity Attacks in Decentralized Federated Load Forecasting

Shabbir,

Manzoor,

Manzoor

et al. 2024

Electronics

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This study examines the impact of data integrity attacks on Federated Learning (FL) for load forecasting in smart grid systems, where privacy-sensitive data require robust management. While FL provides a privacy-preserving approach to distributed model training, it remains susceptible to attacks like data poisoning, which can impair model performance. We compare Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL), using line, ring and bus topologies, under adversarial conditions. Employing a three-layer Artificial Neural Network (ANN) with substation-level datasets (APEhourly,PJMEhourly, and COMEDhourly), we evaluate the system’s resilience in the absence of anomaly detection. Results indicate that DFL significantly outperforms CFL in attack resistance, achieving Mean Absolute Percentage Errors (MAPEs) of 0.48%, 4.29% and 0.702% across datasets, compared to the CFL MAPEs of 6.07%, 18.49% and 10.19%. This demonstrates the potential of DFL as a resilient, secure solution for load forecasting in smart grids, minimizing dependence on anomaly detection to maintain data integrity.

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Resilience of Federated Learning Against False Data Injection Attacks in Energy Forecasting

Cited by 4 publications

References 18 publications

Machine learning‐assisted anomaly detection for power line components: A case study in Pakistan

Machine learning‐assisted anomaly detection for power line components: A case study in Pakistan

Swarm‐Optimized ZnO/CdS/CIGS/GaAs Solar Cell for Enhanced Efficiency and Thermal Resilience

Robustness Against Data Integrity Attacks in Decentralized Federated Load Forecasting

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