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.
@article{arxiv.2302.04363,
title = {Plug In and Learn: Federated Intelligence over a Smart Grid of Models},
author = {S. Abdurakhmanova and Y. SarcheshmehPour and A. Jung},
journal= {arXiv preprint arXiv:2302.04363},
year = {2025}
}