mlsanityData to 7 Oct 2026

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

arXiv 2103.10346 ↗First posted 18 Mar 2021cs.LG cs.DCcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 10 of its 26 references that are in the map. First posted on arXiv 18 Mar 2021.

Builds on

10 of 26 references.

References · 10

  1. 2019Advances and Open Problems in Federated Learningcited by 3,021
  2. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  3. 2019Federated Learning over Wireless Fading Channelscited by 178
  4. 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 168
  5. 2019Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networkscited by 79
  6. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  7. 2016Gossip training for deep learningcited by 33
  8. 2020Decentralized Federated Learning via SGD over Wireless D2D Networkscited by 32
  9. 2019GADMM: Fast and Communication Efficient Framework for Distributed Machine Learningcited by 30
  10. 2020Consensus-Based Distributed Computation of Link-Based Network Metricscited by 2

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 2.

8 papers.

  1. 2022An Energy and Carbon Footprint Analysis of Distributed and Federated Learningcited by 16
  2. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  3. 2021On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networkscited by 10
  4. 2023Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneitycited by 6
  5. 2023DeepEn2023: Energy Datasets for Edge Artificial Intelligencecited by 4
  6. 2024Energy-Aware Decentralized Learning with Intermittent Model Trainingcited by 1
  7. 2022Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Citiescited by 0
  8. 2023An effective and efficient green federated learning method for one-layer neural networkscited by 0