mlsanityData to 7 Oct 2026

Green Federated Learning: A new era of Green Aware AI

arXiv 2409.12626 ↗First posted 19 Sep 2024cs.LGcited by 12

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Abstract (arXiv)

The development of AI applications, especially in large-scale wireless networks, is growing exponentially, alongside the size and complexity of the architectures used. Particularly, machine learning is acknowledged as one of today's most energy-intensive computational applications, posing a significant challenge to the environmental sustainability of next-generation intelligent systems. Achieving environmental sustainability entails ensuring that every AI algorithm is designed with sustainability in mind, integrating green considerations from the architectural phase onwards. Recently, Federated Learning (FL), with its distributed nature, presents new opportunities to address this need. Hence, it's imperative to elucidate the potential and challenges stemming from recent FL advancements and their implications for sustainability. Moreover, it's crucial to furnish researchers, stakeholders, and interested parties with a roadmap to navigate and understand existing efforts and gaps in green-aware AI algorithms. This survey primarily aims to achieve this objective by identifying and analyzing over a hundred FL works, assessing their contributions to green-aware artificial intelligence for sustainable environments, with a specific focus on IoT research. It delves into current issues in green federated learning from an energy-efficient standpoint, discussing potential challenges and future prospects for green IoT application research.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 66 of its 157 references that are in the map. First posted on arXiv 19 Sep 2024.

Builds on

66 of 157 references.

References · 66

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
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  11. 2021Model-Contrastive Federated Learningcited by 762
  12. 2017Channel Pruning for Accelerating Very Deep Neural Networkscited by 697
38 more
  1. 2017Learning Efficient Convolutional Networks through Network Slimmingcited by 673
  2. 2019Green AIcited by 620
  3. 2020Federated Learning with Matched Averagingcited by 564
  4. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  5. 2019Federated Learning with Personalization Layerscited by 533
  6. 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
  7. 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 427
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  9. 2018Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Accelerationcited by 365
  10. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  11. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  12. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  13. 2020HRank: Filter Pruning using High-Rank Feature Mapcited by 291
  14. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  15. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 246
  16. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  17. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  18. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 159
  19. 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 143
  20. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  21. 2020Optimal Client Sampling for Federated Learningcited by 123
  22. 2019Overcoming Forgetting in Federated Learning on Non-IID Datacited by 120
  23. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  24. 2022Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learningcited by 81
  25. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 80
  26. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  27. 2020DMCP: Differentiable Markov Channel Pruning for Neural Networkscited by 68
  28. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  29. 2020GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphscited by 64
  30. 2020Fast-Convergent Federated Learningcited by 61
  31. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  32. 2021Adaptive Quantization of Model Updates for Communication-Efficient Federated Learningcited by 60
  33. 2020Ternary Compression for Communication-Efficient Federated Learningcited by 57
  34. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  35. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  36. 2020Can Federated Learning Save The Planet?cited by 52
  37. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  38. 2021AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learningcited by 39

50 most cited of 66 All 66 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 7.

12 papers.

  1. 2025Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learningcited by 2
  2. 2025UltraFlwr -- An Efficient Federated Surgical Object Detection Frameworkcited by 0
  3. 2025Progressive Size-Adaptive Federated Learning: A Comprehensive Framework for Heterogeneous Multi-Modal Data Systemscited by 0
  4. 2025Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Schedulingcited by 0
  5. 2025Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Researchcited by 0
  6. 2026Standardized Methods and Recommendations for Green Federated Learningcited by 0
  7. 2026Distributed LLM Pretraining During Renewable Curtailment Windows: A Feasibility Studycited by 0
  8. 2026Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directionscited by 0
  9. 2026VARS-FL: Validation-Aligned Client Selection for Non-IID Federated Learning in IoT Systemscited by 0
  10. 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networkscited by 0
  11. 2026Federated Attention Autoencoders with a Stochastic Aggregation Scheme for Anomaly Detectioncited by 0
  12. 2026A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraintscited by 0