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arXiv · 2302.04363

Plug In and Learn: Federated Intelligence over a Smart Grid of Models

S. Abdurakhmanova·Y. SarcheshmehPour·A. Jung
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Abstract

We present a model-agnostic federated learning method that mirrors the operation of a smart power grid: diverse local models, like energy prosumers, train independently on their own data while exchanging lightweight signals to coordinate with statistically similar peers. This coordination is governed by a graph-based regularizer that encourages connected models to produce similar predictions on a shared, public unlabeled dataset. The resulting method is a flexible instance of regularized empirical risk minimization and supports a wide variety of local models - both parametric and non-parametric - provided they can be trained via regularized loss minimization. Such training is readily supported by standard ML libraries including scikit-learn, Keras, and PyTorch.

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BibTeXRIS

S. Abdurakhmanova, Y. SarcheshmehPour, A. Jung. 2023-02-08. Plug In and Learn: Federated Intelligence over a Smart Grid of Models. https://arxiv.org/abs/2302.04363

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