Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling
Not on the map. Why?
Abstract (arXiv)
Training large-scale machine learning models incurs substantial carbon emissions. Federated Learning (FL), by distributing computation across geographically dispersed clients, offers a natural framework to leverage regional and temporal variations in Carbon Intensity (CI). This paper investigates how to reduce emissions in FL through carbon-aware client selection and training scheduling. We first quantify the emission savings of a carbon-aware scheduling policy that leverages slack time -- permitting a modest extension of the training duration so that clients can defer local training rounds to lower-carbon periods. We then examine the performance trade-offs of such scheduling which stem from statistical heterogeneity among clients, selection bias in participation, and temporal correlation in model updates. To leverage these trade-offs, we construct a carbon-aware scheduler that integrates slack time, α-fair carbon allocation, and a global fine-tuning phase. Experiments on real-world CI data show that our scheduler outperforms slack-agnostic baselines, achieving higher model accuracy across a wide range of carbon budgets, with especially strong gains under tight carbon constraints.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 10 Sep 2025.
Builds on
23 of 33 references.
References · 23
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
- 2019Advances and Open Problems in Federated Learningcited by 3,046
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
- 2023A Simple and Effective Pruning Approach for Large Language Modelscited by 800
- 2019Green AIcited by 627
- 2019Fair Resource Allocation in Federated Learningcited by 394
- 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
- 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 198
- 2020Optimal Client Sampling for Federated Learningcited by 124
- 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 111
11 more
- 2020TiFL: A Tier-based Federated Learning Systemcited by 96
- 2018SqueezeNext: Hardware-Aware Neural Network Designcited by 61
- 2022Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AIcited by 58
- 2020Can Federated Learning Save The Planet?cited by 52
- 2021Last iterate convergence of SGD for Least-Squares in the Interpolation regimecited by 42
- 2022Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenterscited by 32
- 2021Resource-Efficient Federated Learningcited by 26
- 2023Federated Learning under Heterogeneous and Correlated Client Availabilitycited by 18
- 2021Enabling Sustainable Clouds: The Case for Virtualizing the Energy Systemcited by 12
- 2023FedZero: Leveraging Renewable Excess Energy in Federated Learningcited by 11
- 2023CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centerscited by 6
Built on by
Citing papers per year: 2026: 2.
2 papers.