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

• URL: http://arxiv.org/abs/2302.04363v4
• Date: Thu, 29 May 2025 07:27:48 GMT
• Title: Plug In and Learn: Federated Intelligence over a Smart Grid of Models
• Abstract summary: We present a model-agnostic federated learning method that mirrors the operation of a smart power grid.<n> diverse local models, like energy prosumers, train independently on their own data while exchanging lightweight signals to coordinate with statistically similar peers.<n>This coordination is governed by a graph-based regularizer that encourages connected models to produce similar predictions on a shared, public unlabeled dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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