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arXiv Machine Learning By Jianing Chen, Vajiheh Farhadi, Yan Li, Thomas La Porta

Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization

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The paper investigates how model initialization affects federated short‑term load forecasting (STLF) when client data are heterogeneous. It introduces two strategies: a global pretrained initialization using auxiliary public load data to reduce client drift, and a local sequential initialization (SLIAvg) that lets clients start from progressively adapted models each round. Experiments on real smart‑meter data show these methods improve convergence and lower forecasting errors while remaining compatible with existing federated learning frameworks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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