mlsanityData to 14 Oct 2026

Edge AI for Internet of Energy: Challenges and Perspectives

arXiv 2311.16851 ↗First posted 28 Nov 2023cs.NI cs.AI cs.CYcited by 1

Not on the map. Why?

Abstract (arXiv)

The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI). This comprehensive review elucidates the promise and potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a meticulously curated research methodology, the article delves into the myriad of edge AI techniques specifically tailored for IoE. The myriad benefits, spanning from reduced latency and real-time analytics to the pivotal aspects of information security, scalability, and cost-efficiency, underscore the indispensability of edge AI in modern IoE frameworks. As the narrative progresses, readers are acquainted with pragmatic applications and techniques, highlighting on-device computation, secure private inference methods, and the avant-garde paradigms of AI training on the edge. A critical analysis follows, offering a deep dive into the present challenges including security concerns, computational hurdles, and standardization issues. However, as the horizon of technology ever expands, the review culminates in a forward-looking perspective, envisaging the future symbiosis of 5G networks, federated edge AI, deep reinforcement learning, and more, painting a vibrant panorama of what the future beholds. For anyone vested in the domains of IoE and AI, this review offers both a foundation and a visionary lens, bridging the present realities with future possibilities.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 73 of its 343 references that are in the map. First posted on arXiv 28 Nov 2023.

Builds on

73 of 343 references.

References · 73

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  3. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2020Knowledge Distillation: A Surveycited by 1,603
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  7. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  8. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,060
  9. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
  10. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
  11. 2018Sparsified SGD with Memorycited by 376
  12. 2019Error Feedback Fixes SignSGD and other Gradient Compression Schemescited by 310
38 more
  1. 2017A Survey of Model Compression and Acceleration for Deep Neural Networkscited by 301
  2. 2017TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learningcited by 256
  3. 2017Sparse Communication for Distributed Gradient Descentcited by 249
  4. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 228
  5. 2017Gradient Sparsification for Communication-Efficient Distributed Optimizationcited by 201
  6. 2018Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysiscited by 156
  7. 2017Distributed Deep Neural Networks over the Cloud, the Edge and End Devicescited by 120
  8. 2018A generic framework for privacy preserving deep learningcited by 103
  9. 2019Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligencecited by 76
  10. 2017Deep Learning for IoT Big Data and Streaming Analytics: A Surveycited by 59
  11. 2023Large Language Models Empowered Autonomous Edge AI for Connected Intelligencecited by 56
  12. 2019On Safeguarding Privacy and Security in the Framework of Federated Learningcited by 55
  13. 2018Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergycited by 51
  14. 2019QUOTIENT: Two-Party Secure Neural Network Training and Predictioncited by 41
  15. 2017An Entropy-based Pruning Method for CNN Compressioncited by 37
  16. 2018Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platformscited by 35
  17. 2019Deep Reinforcement Learning for Smart Home Energy Managementcited by 30
  18. 2020EdgeRec: Recommender System on Edge in Mobile Taobaocited by 29
  19. 2018Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloudcited by 28
  20. 2018Leveraging Filter Correlations for Deep Model Compressioncited by 23
  21. 2020A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devicescited by 21
  22. 2018Privacy-preserving Machine Learning through Data Obfuscationcited by 18
  23. 2023Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directionscited by 18
  24. 2020Online Ensemble Model Compression using Knowledge Distillationcited by 16
  25. 2019Play and Prune: Adaptive Filter Pruning for Deep Model Compressioncited by 15
  26. 2018All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Surveycited by 14
  27. 2023Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challengescited by 14
  28. 2019Edge-Computing-Enabled Smart Cities: A Comprehensive Surveycited by 13
  29. 2017When Mobile Blockchain Meets Edge Computingcited by 11
  30. 2020Serdab: An IoT Framework for Partitioning Neural Networks Computation across Multiple Enclavescited by 11
  31. 2021A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learningcited by 11
  32. 2021Blockchain-based Recommender Systems: Applications, Challenges and Future Opportunitiescited by 11
  33. 2021DER Forecast using Privacy Preserving Federated Learningcited by 10
  34. 2015EdgeCentric: Anomaly Detection in Edge-Attributed Networkscited by 9
  35. 2020A Survey on Security and Privacy Issues in Edge Computing-Assisted Internet of Thingscited by 9
  36. 2020The emergence of Explainability of Intelligent Systems: Delivering Explainable and Personalised Recommendations for Energy Efficiencycited by 9
  37. 2023DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimizationcited by 9
  38. 2021Intelligent edge-based recommender system for internet of energy applicationscited by 7

50 most cited of 73 All 73 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.