Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers

• URL: http://arxiv.org/abs/2404.01517v1
• Date: Mon, 1 Apr 2024 22:53:09 GMT
• Title: Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers
• Authors: Shourya Bose, Yu Zhang, Kibaek Kim,
• Abstract summary: We propose the use of personalization layers for load forecasting in a general framework called PL-FL. We show that PL-FL outperforms FL and purely local training, while requiring lower communication bandwidth than FL.
• Score: 3.933147844455233
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advent of smart meters has enabled pervasive collection of energy consumption data for training short-term load forecasting models. In response to privacy concerns, federated learning (FL) has been proposed as a privacy-preserving approach for training, but the quality of trained models degrades as client data becomes heterogeneous. In this paper we propose the use of personalization layers for load forecasting in a general framework called PL-FL. We show that PL-FL outperforms FL and purely local training, while requiring lower communication bandwidth than FL. This is done through extensive simulations on three different datasets from the NREL ComStock repository.

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