Federated Clustering: A Privacy-Preserving Approach for Electricity Consumption Pattern Extraction

‘Federated clustering for electricity consumption pattern extraction’ presents a novel method for extracting shared patterns or trends in electricity consumption data from various regions or users using federated clustering algorithms. Traditional clustering algorithms require centralized data access and processing, raising privacy and security concerns, especially when dealing with personal electricity consumption data. This paper introduces a federated clustering approach that allows clustering analysis of distributed data without sharing raw information.

Federated clustering algorithms rely on distributed computing and privacy-preserving techniques, enabling participants to perform data clustering locally on their computers and then share only the clustering results, not the original data. This approach aims to protect individual privacy while achieving cross-regional or cross-user clustering analysis.

Through this research, we gain insights into the application of federated clustering in analyzing electricity consumption data and explore solutions for balancing individual privacy protection with global pattern extraction. This methodology holds immense potential for transforming energy and related sectors, offering new avenues for analyzing and optimizing electricity consumption.