Fractal-Aware Federated AI for Personalized Demand-Side Management: A Review of Household Heterogeneity, Privacy, and Flexibility Learning

Abstract

Federated artificial intelligence is increasingly viewed as a promising approach for privacy-preserving demand-side management because it enables distributed learning from household energy data without centralizing raw smart-meter records. However, existing federated energy models often treat household heterogeneity mainly as a non-IID statistical problem, while overlooking the multiscale behavioral structure of electricity demand. This paper proposes a fractal-aware federated AI framework for demand-side management, focusing on household heterogeneity, privacy, and personalized flexibility learning. This paper synthesizes literature on federated learning, smart-meter analytics, fractal and multifractal demand characterization, privacy-preserving energy management, and personalized demand response. The proposed framework argues that fractal indicators, such as persistence, irregularity, long-range dependence, and scale-dependent variability, can enrich federated models by capturing household-specific demand signatures. These indicators may support more accurate flexibility prediction, cluster-aware personalization, privacy-conscious consumer profiling, and adaptive incentive design. The paper contributes a conceptual foundation for future empirical research on federated DSM systems that are not only privacy-preserving, but also personalized, interpretable, behaviorally aware, and fair across heterogeneous households.