mlsanityData to 7 Oct 2026

FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing

arXiv 2111.06146 ↗First posted 11 Nov 2021cs.LGcited by 4

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Abstract (arXiv)

Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without uploading the local data. Gradient compression may be applied to FL to alleviate the communication overheads but current FL with gradient compression still faces great challenges. To deploy green MEC, we propose FedGreen, which enhances the original FL with fine-grained gradient compression to efficiently control the total energy consumption of the devices. Specifically, we introduce the relevant operations including device-side gradient reduction and server-side element-wise aggregation to facilitate the gradient compression in FL. According to a public dataset, we investigate the contributions of the compressed local gradients with respect to different compression ratios. After that, we formulate and tackle a learning accuracy-energy efficiency tradeoff problem where the optimal compression ratio and computing frequency are derived for each device. Experiments results demonstrate that given the 80% test accuracy requirement, compared with the baseline schemes, FedGreen reduces at least 32% of the total energy consumption of the devices.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 15 of its 21 references that are in the map. First posted on arXiv 11 Nov 2021.

Builds on

15 of 21 references.

References · 15

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  6. 2020Federated Learning with Matched Averagingcited by 564
  7. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  8. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  9. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  10. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  11. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 103
  12. 2020Multi-Armed Bandit Based Client Scheduling for Federated Learningcited by 56
3 more
  1. 2020To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devicescited by 45
  2. 2021Towards Energy Efficient Federated Learning over 5G+ Mobile Devicescited by 16
  3. 2021FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computingcited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 2.

4 papers.

  1. 2024Green Federated Learning: A new era of Green Aware AIcited by 12
  2. 2023AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devicescited by 10
  3. 2026Standardized Methods and Recommendations for Green Federated Learningcited by 0
  4. 2026Designing Sustainable Federated Learning as a Service using Neural Architecture Searchcited by 0