mlsanityData to 7 Oct 2026

Can Federated Learning Save The Planet?

arXiv 2010.06537 ↗First posted 13 Oct 2020cs.LGcited by 52

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Abstract (arXiv)

Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL, in particular, is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and the civil society for privacy protection. However, the potential environmental impact related to FL remains unclear and unexplored. This paper offers the first-ever systematic study of the carbon footprint of FL. First, we propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. Then, we compare the carbon footprint of FL to traditional centralized learning. Our findings show FL, despite being slower to converge, can be a greener technology than data center GPUs. Finally, we highlight and connect the reported results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 14; 2023: 7; 2024: 13; 2025: 8; 2026: 5.

52 papers.

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  1. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  2. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  3. 2022ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsitycited by 45
  4. 2021DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learningcited by 40
  5. 2021End-to-End Speech Recognition from Federated Acoustic Modelscited by 30
  6. 2022Gradient Masked Averaging for Federated Learningcited by 22
  7. 2022An Energy and Carbon Footprint Analysis of Distributed and Federated Learningcited by 16
  8. 2024Federated Learning Priorities Under the European Union Artificial Intelligence Actcited by 13
  9. 2024Green Federated Learning: A new era of Green Aware AIcited by 12
  10. 2022Towards Energy-Aware Federated Learning on Battery-Powered Clientscited by 11
  11. 2023FedZero: Leveraging Renewable Excess Energy in Federated Learningcited by 11
  12. 2022Federated Self-supervised Speech Representations: Are We There Yet?cited by 10
38 more
  1. 2021Towards Quantifying the Carbon Emissions of Differentially Private Machine Learningcited by 8
  2. 2023Green Federated Learningcited by 8
  3. 2024A Nested Model for AI Design and Validationcited by 8
  4. 2022FedSyn: Synthetic Data Generation using Federated Learningcited by 7
  5. 2025CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimizationcited by 7
  6. 2025Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languagescited by 5
  7. 2022(Im)possibility of Collective Intelligencecited by 4
  8. 2022FedorAS: Federated Architecture Search under system heterogeneitycited by 4
  9. 2022Reconciling Security and Communication Efficiency in Federated Learningcited by 4
  10. 2024Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inferencecited by 4
  11. 2022Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-runningcited by 3
  12. 2023A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsificationcited by 3
  13. 2024PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computationcited by 3
  14. 2024FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clientscited by 2
  15. 2024Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Studycited by 2
  16. 2025Green Federated Learning via Carbon-Aware Client and Time Slot Schedulingcited by 2
  17. 2022Scheduling Algorithms for Federated Learning with Minimal Energy Consumptioncited by 1
  18. 2022On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learningcited by 1
  19. 2023EcoLearn: Optimizing the Carbon Footprint of Federated Learningcited by 1
  20. 2024Energy-efficient Decentralized Learning via Graph Sparsificationcited by 1
  21. 2024Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settingscited by 1
  22. 2024FedGreen: Carbon-aware Federated Learning with Model Size Adaptationcited by 1
  23. 2024On Vessel Location Forecasting and the Effect of Federated Learningcited by 1
  24. 2024Energy-Aware Decentralized Learning with Intermittent Model Trainingcited by 1
  25. 2025Time-varying Mixing Matrix Design for Energy-efficient Decentralized Federated Learningcited by 1
  26. 2026On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learningcited by 1
  27. 2022Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerationscited by 0
  28. 2022GTFLAT: Game Theory Based Add-On For Empowering Federated Learning Aggregation Techniquescited by 0
  29. 2023Energy Concerns with HPC Systems and Applicationscited by 0
  30. 2023An effective and efficient green federated learning method for one-layer neural networkscited by 0
  31. 2023Holistic analysis on the sustainability of Federated Learning across AI product lifecyclecited by 0
  32. 2024SADDLe: Sharpness-Aware Decentralized Deep Learning with Heterogeneous Datacited by 0
  33. 2025Phoeni6: a Systematic Approach for Evaluating the Energy Consumption of Neural Networkscited by 0
  34. 2025Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learningcited by 0
  35. 2025Exploring Federated Learning for Thermal Urban Feature Segmentation -- A Comparison of Centralized and Decentralized Approachescited by 0
  36. 2025Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attemptscited by 0
  37. 2026Standardized Methods and Recommendations for Green Federated Learningcited by 0
  38. 2026MedicalRec: Medical recommender system for image classification without retrainingcited by 0

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