International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 6 (November-December 2025) Submit your research before last 3 days of December to publish your research paper in the issue of November-December.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 6
November-December 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Can Federated Learning Enable Environmentally Sustainable AI by Reducing Centralized Data Storage and Transmission Costs?

Author(s) Asbah Ayub
Country India
Abstract AI uses a lot of electricity because we store huge datasets in data centres and move many gigabytes across networks. The International Energy Agency (IEA) projects that electricity use by data centres could more than double to roughly 945 TWh by 2030, with AI as a key driver. Data transmission networks also consume large amounts of power—about 260–360 TWh in 2022—so every extra gigabyte moved has an energy and carbon cost.
Federated learning (FL) trains models by keeping data on local devices or sites and sending only model updates to a server. In simple terms, this can cut centralised storage and reduce raw-data transfers. If fewer raw bytes are shipped and stored centrally, we can lower some network and storage energy. However, federated learning(FL) adds new costs: more rounds of communication, on-device computation, and the carbon intensity of where and when devices and servers run. Recent research shows the result is not automatic—depending on settings, FL can even emit much more carbon than centralised training (for example, with many communication rounds, poor device efficiency, or carbon-heavy grids).This paper will tests a simple idea that is federated learning( FL) can support environmentally sustainable AI when designs minimise communication (e.g., compression, fewer rounds), schedule training in low-carbon locations and times, and use energy-efficient devices and servers. Emerging “green FL” methods and carbon-aware schedulers try to do this by adapting model size and timing to local grid carbon intensity.
Keywords federated learning; sustainable AI; data centres; network energy; carbon footprint; communication efficiency; carbon-aware scheduling.
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-29
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.61873
Short DOI https://doi.org/hbdsrn
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 5Cover Page Vol 7 Isu 4Cover Page Vol 7 Isu 3

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com