# A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning

arXiv: https://arxiv.org/abs/2103.10346 · HTML: https://mlsanity.com/paper/2103.10346/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks](https://mlsanity.com/paper/1912.13163/) (2019)
- [Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems](https://mlsanity.com/paper/2101.03367/) (2021)
- [Gossip training for deep learning](https://mlsanity.com/paper/1611.09726/) (2016)
- [Decentralized Federated Learning via SGD over Wireless D2D Networks](https://mlsanity.com/paper/2002.12507/) (2020)
- [GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning](https://mlsanity.com/paper/1909.00047/) (2019)
- [Consensus-Based Distributed Computation of Link-Based Network Metrics](https://mlsanity.com/paper/2012.14971/) (2020)

## Built on by (most cited first, 8 of 8)

- [An Energy and Carbon Footprint Analysis of Distributed and Federated Learning](https://mlsanity.com/paper/2206.10380/) (2022)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks](https://mlsanity.com/paper/2111.07911/) (2021)
- [Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity](https://mlsanity.com/paper/2312.04504/) (2023)
- [DeepEn2023: Energy Datasets for Edge Artificial Intelligence](https://mlsanity.com/paper/2312.00103/) (2023)
- [Energy-Aware Decentralized Learning with Intermittent Model Training](https://mlsanity.com/paper/2407.01283/) (2024)
- [Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities](https://mlsanity.com/paper/2201.07980/) (2022)
- [An effective and efficient green federated learning method for one-layer neural networks](https://mlsanity.com/paper/2312.14528/) (2023)

## Abstract (arXiv)

Recent advances in distributed learning raise environmental concerns due to the large energy needed to train and move data to/from data centers. Novel paradigms, such as federated learning (FL), are suitable for decentralized model training across devices or silos that simultaneously act as both data producers and learners. Unlike centralized learning (CL) techniques, relying on big-data fusion and analytics located in energy hungry data centers, in FL scenarios devices collaboratively train their models without sharing their private data. This article breaks down and analyzes the main factors that influence the environmental footprint of FL policies compared with classical CL/Big-Data algorithms running in data centers. The proposed analytical framework takes into account both learning and communication energy costs, as well as the carbon equivalent emissions; in addition, it models both vanilla and decentralized FL policies driven by consensus. The framework is evaluated in an industrial setting assuming a real-world robotized workplace. Results show that FL allows remarkable end-to-end energy savings (30%-40%) for wireless systems characterized by low bit/Joule efficiency (50 kbit/Joule or lower). Consensus-driven FL does not require the parameter server and further reduces emissions in mesh networks (200 kbit/Joule). On the other hand, all FL policies are slower to converge when local data are unevenly distributed (often 2x slower than CL). Energy footprint and learning loss can be traded off to optimize efficiency.