Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Gholizadeh, Nastaran; Musilek, Petr

doi:10.3390/en14123654

Cited by 10 publications

(5 citation statements)

References 119 publications

(117 reference statements)

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“…These updated weights are subsequently shared back with the edge devices, initiating another round of training iterations. This iterative cycle continues until the desired model accuracy is achieved [21]. What sets FL apart is its exceptional ability to preserve data privacy.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Adversarial Attack Resilience in Energy Networks through Energy and Privacy Aware Federated Learning

Manzoor,

Arshad,

Assaleh

et al. 2024

Preprint

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The integration of artificial intelligence (AI) into energy networks significantly advanced short-term forecasting, particularly in smart meter applications. However, as distributed energy resources proliferated and energy systems grew in complexity, traditional centralized approaches to data analysis became insufficient in addressing privacy-preserving challenges. Federated learning (FL) emerged as a promising solution, leveraging distributed data sources while safeguarding user privacy. Nonetheless, FL encountered inherent vulnerabilities to adversarial attacks during model training, undermining its reliability and effectiveness. Existing techniques to eliminate these attacks often required additional frameworks for detection, imposing an added burden on devices. To address this issue, we proposed a novel method called federated random layer aggregation (FedRLA). It aggregated only one randomly chosen neural network layer on the server in a privacy-aware manner, leaving the remaining layers unchanged. FedRLA exhibited superior resilience against adversarial attacks by confining attackers to a single neural network layer. Our simulations, focusing on household energy consumption, demonstrated that FedRLA achieved 3.56 times less data transmission compared to FedAvg during global model training. This enhanced efficiency translated to improved energy usage and resource conservation. Furthermore, FedRLA performed better in the presence of differential privacy under attack and no attack conditions.

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Section: Introductionmentioning

confidence: 99%

Enhanced Adversarial Attack Resilience in Energy Networks through Energy and Privacy Aware Federated Learning

Manzoor,

Arshad,

Assaleh

et al. 2024

Preprint

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“…L ARGE-scale penetration of electric vehicles and integration of renewable energy resources have increased the complexity of power distribution networks [1]. As a result, more advanced control and optimization techniques are required to keep the system within operational constraints, reduce losses, and supply demands [2].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling

2023

Self Cite

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Distribution network reconfiguration (DNR) is one of the most important methods to cope with the increasing electricity demand due to the massive integration of electric vehicles. Most existing DNR methods rely on accurate network parameters and lack scalability and optimality. This study uses model-free reinforcement learning algorithms for training agents to take the best DNR actions in a given distribution system. Five reinforcement algorithms are applied to the DNR problem in 33-and 136-node test systems and their performances are compared: deep Q-learning, dueling deep Q-learning, deep Q-learning with prioritized experience replay, soft actor-critic, and proximal policy optimization. In addition, a new deep Q-learning-based action sampling method is developed to reduce the size of the action space and optimize the loss reduction in the system. Finally, the developed algorithms are compared against the existing methods in literature.

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“…On the other hand, the paper [ 23 ] proposed a hierarchical approach in classifier ensembles. Mainly in the literature, distributed learning is considered in terms of the following issues [ 2 , 24 ]: data division—horizontal or vertical fragmentation; type of base classifiers—can be homogeneous or heterogeneous; type and cost of communication—data or models may be shared; privacy and data security—whether raw data exchange is allowed; fusion methods—if local models are built (global model is not created) then fusion of predictions is necessary to generate global decisions; data consistency—it can be assumed that objects are shared between local tables and are consistent, or data can be independently created and inconsistent. However, proposed approaches do not analyze the contents of local tables and the relationships between them.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, conflict analysis is widely considered and various models are proposed. Group decision-making represents an approach that solves the situation in which each individual has their own private perspective [ 24 ]. In [ 25 ], a model is proposed for distributed group-decision support system that is suitable for use over the Internet.…”

Section: Introductionmentioning

confidence: 99%

New Classification Method for Independent Data Sources Using Pawlak Conflict Model and Decision Trees

Przybyła–Kasperek

Kusztal

2022

Entropy

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The research concerns data collected in independent sets—more specifically, in local decision tables. A possible approach to managing these data is to build local classifiers based on each table individually. In the literature, many approaches toward combining the final prediction results of independent classifiers can be found, but insufficient efforts have been made on the study of tables’ cooperation and coalitions’ formation. The importance of such an approach was expected on two levels. First, the impact on the quality of classification—the ability to build combined classifiers for coalitions of tables should allow for the learning of more generalized concepts. In turn, this should have an impact on the quality of classification of new objects. Second, combining tables into coalitions will result in reduced computational complexity—a reduced number of classifiers will be built. The paper proposes a new method for creating coalitions of local tables and generating an aggregated classifier for each coalition. Coalitions are generated by determining certain characteristics of attribute values occurring in local tables and applying the Pawlak conflict analysis model. In the study, the classification and regression trees with Gini index are built based on the aggregated table for one coalition. The system bears a hierarchical structure, as in the next stage the decisions generated by the classifiers for coalitions are aggregated using majority voting. The classification quality of the proposed system was compared with an approach that does not use local data cooperation and coalition creation. The structure of the system is parallel and decision trees are built independently for local tables. In the paper, it was shown that the proposed approach provides a significant improvement in classification quality and execution time. The Wilcoxon test confirmed that differences in accuracy rate of the results obtained for the proposed method and results obtained without coalitions are significant, with a p level = 0.005. The average accuracy rate values obtained for the proposed approach and the approach without coalitions are, respectively: 0.847 and 0.812; so the difference is quite large. Moreover, the algorithm implementing the proposed approach performed up to 21-times faster than the algorithm implementing the approach without using coalitions.

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Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Cited by 10 publications

References 119 publications

Enhanced Adversarial Attack Resilience in Energy Networks through Energy and Privacy Aware Federated Learning

Enhanced Adversarial Attack Resilience in Energy Networks through Energy and Privacy Aware Federated Learning

A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling

New Classification Method for Independent Data Sources Using Pawlak Conflict Model and Decision Trees

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