# An effective and efficient green federated learning method for one-layer neural networks

arXiv: https://arxiv.org/abs/2312.14528 · HTML: https://mlsanity.com/paper/2312.14528/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning](https://mlsanity.com/paper/2008.03606/) (2020)
- [Learning Deep Kernels for Non-Parametric Two-Sample Tests](https://mlsanity.com/paper/2002.09116/) (2020)
- [Can Federated Learning Save The Planet?](https://mlsanity.com/paper/2010.06537/) (2020)
- [A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning](https://mlsanity.com/paper/2103.10346/) (2021)
- [Federated AI for building AI Solutions across Multiple Agencies](https://mlsanity.com/paper/1809.10036/) (2018)
- [Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS](https://mlsanity.com/paper/1807.06996/) (2018)

## Abstract (arXiv)

Nowadays, machine learning algorithms continue to grow in complexity and require a substantial amount of computational resources and energy. For these reasons, there is a growing awareness of the development of new green algorithms and distributed AI can contribute to this. Federated learning (FL) is one of the most active research lines in machine learning, as it allows the training of collaborative models in a distributed way, an interesting option in many real-world environments, such as the Internet of Things, allowing the use of these models in edge computing devices. In this work, we present a FL method, based on a neural network without hidden layers, capable of generating a global collaborative model in a single training round, unlike traditional FL methods that require multiple rounds for convergence. This allows obtaining an effective and efficient model that simplifies the management of the training process. Moreover, this method preserve data privacy by design, a crucial aspect in current data protection regulations. We conducted experiments with large datasets and a large number of federated clients. Despite being based on a network model without hidden layers, it maintains in all cases competitive accuracy results compared to more complex state-of-the-art machine learning models. Furthermore, we show that the method performs equally well in both identically and non-identically distributed scenarios. Finally, it is an environmentally friendly algorithm as it allows significant energy savings during the training process compared to its centralized counterpart.