mlsanityData to 14 Oct 2026

Federated Learning for Energy Constrained IoT devices: A systematic mapping study

arXiv 2301.03720 ↗First posted 9 Jan 2023cs.LG cs.DCcited by 0

Not on the map. Why?

Abstract (arXiv)

Federated Machine Learning (Fed ML) is a new distributed machine learning technique applied to collaboratively train a global model using clients local data without transmitting it. Nodes only send parameter updates (e.g., weight updates in the case of neural networks), which are fused together by the server to build the global model. By not divulging node data, Fed ML guarantees its confidentiality, a crucial aspect of network security, which enables it to be used in the context of data-sensitive Internet of Things (IoT) and mobile applications, such as smart Geo-location and the smart grid. However, most IoT devices are particularly energy constrained, which raises the need to optimize the Fed ML process for efficient training tasks and optimized power consumption. In this paper, we conduct, to the best of our knowledge, the first Systematic Mapping Study (SMS) on Fed ML optimization techniques for energy-constrained IoT devices. From a total of more than 800 papers, we select 67 that satisfy our criteria and give a structured overview of the field using a set of carefully chosen research questions. Finally, we attempt to provide an analysis of the energy-constrained Fed ML state of the art and try to outline some potential recommendations for the research community.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 50 of its 97 references that are in the map. First posted on arXiv 9 Jan 2023.

Builds on

50 of 97 references.

References · 50

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,262
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  6. 2018Federated Learning with Non-IID Datacited by 1,108
  7. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
  8. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  9. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  10. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  11. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  12. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
38 more
  1. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  2. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  3. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 337
  4. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 243
  5. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 222
  6. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  7. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 179
  8. 2020Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Datacited by 125
  9. 2019TBT: Targeted Neural Network Attack with Bit Trojancited by 113
  10. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 100
  11. 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
  12. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 81
  13. 2018Privacy Preserving Machine Learning: Threats and Solutionscited by 79
  14. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  15. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  16. 2021Adaptive Quantization of Model Updates for Communication-Efficient Federated Learningcited by 63
  17. 2020Ternary Compression for Communication-Efficient Federated Learningcited by 57
  18. 2019Understanding Top-k Sparsification in Distributed Deep Learningcited by 56
  19. 2020Federated Knowledge Distillationcited by 51
  20. 2020To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devicescited by 46
  21. 2020Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learningcited by 38
  22. 2021GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learningcited by 36
  23. 2020Energy-Efficient Federated Edge Learning with Joint Communication and Computation Designcited by 30
  24. 2020Communication-Efficient Federated Distillationcited by 28
  25. 2021FEDZIP: A Compression Framework for Communication-Efficient Federated Learningcited by 27
  26. 2020Faster On-Device Training Using New Federated Momentum Algorithmcited by 25
  27. 2019Energy-Aware Analog Aggregation for Federated Learning with Redundant Datacited by 23
  28. 2019Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learningcited by 17
  29. 2020A Systematic Literature Review on Federated Learning: From A Model Quality Perspectivecited by 15
  30. 2021Communication-Efficient Federated Learning with Dual-Side Low-Rank Compressioncited by 10
  31. 2018Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approachcited by 9
  32. 2020Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Datacited by 9
  33. 2021Energy-aware Resource Management for Federated Learning in Multi-access Edge Computing Systemscited by 7
  34. 2019Distilling On-Device Intelligence at the Network Edgecited by 5
  35. 2020A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compressioncited by 5
  36. 2021Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraintscited by 5
  37. 2020CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learningcited by 1
  38. 2021A Technical Report for Light-Edge: A Lightweight Authentication Protocol for IoT Devices in an Edge-Cloud Environmentcited by 1

Built on by

Not cited yet.