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

arXiv: https://arxiv.org/abs/2302.04363 · HTML: https://mlsanity.com/paper/2302.04363/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 8 Feb 2023.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Learning graphs from data: A signal representation perspective](https://mlsanity.com/paper/1806.00848/) (2018)
- [Parameterized Knowledge Transfer for Personalized Federated Learning](https://mlsanity.com/paper/2111.02862/) (2021)
- [Optimal Algorithms for Non-Smooth Distributed Optimization in Networks](https://mlsanity.com/paper/1806.00291/) (2018)
- [Towards Model Agnostic Federated Learning Using Knowledge Distillation](https://mlsanity.com/paper/2110.15210/) (2021)
- [Learning Sparse Graphs Under Smoothness Prior](https://mlsanity.com/paper/1609.03448/) (2016)
- [Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer](https://mlsanity.com/paper/2109.08119/) (2021)
- [Distributed Learning Systems with First-order Methods](https://mlsanity.com/paper/2104.05245/) (2021)
- [Local Graph Clustering with Network Lasso](https://mlsanity.com/paper/2004.12199/) (2020)
- [Vector-Valued Graph Trend Filtering with Non-Convex Penalties](https://mlsanity.com/paper/1905.12692/) (2019)
- [Semi-supervised Learning in Network-Structured Data via Total Variation Minimization](https://mlsanity.com/paper/1901.09838/) (2019)
- [Localized Linear Regression in Networked Data](https://mlsanity.com/paper/1903.11178/) (2019)
- [When is Network Lasso Accurate?](https://mlsanity.com/paper/1704.02107/) (2017)
- [flow-based clustering and spectral clustering: a comparison](https://mlsanity.com/paper/2206.10019/) (2022)

## Built on by (most cited first, 1 of 1)

- [Analysis of Total Variation Minimization for Clustered Federated Learning](https://mlsanity.com/paper/2403.06298/) (2024)

## Abstract (arXiv)

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.