mlsanityData to 14 Oct 2026

Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness

arXiv 2108.03980 ↗First posted 30 Jul 2021cs.DC cs.LGcited by 15

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Abstract (arXiv)

Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology. Decentralized deep learning (DDL) such as federated learning and swarm learning as a promising solution to privacy-preserving data processing for millions of smart edge devices, leverages distributed computing of multi-layer neural networks within the networking of local clients, whereas, without disclosing the original local training data. Notably, in industries such as finance and healthcare where sensitive data of transactions and personal medical records is cautiously maintained, DDL can facilitate the collaboration among these institutes to improve the performance of trained models while protecting the data privacy of participating clients. In this survey paper, we demonstrate the technical fundamentals of DDL that benefit many walks of society through decentralized learning. Furthermore, we offer a comprehensive overview of the current state-of-the-art in the field by outlining the challenges of DDL and the most relevant solutions from novel perspectives of communication efficiency and trustworthiness.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 26 of its 66 references that are in the map. First posted on arXiv 30 Jul 2021.

Builds on

26 of 66 references.

References · 26

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  3. 2018Federated Learning with Non-IID Datacited by 1,108
  4. 2018How To Backdoor Federated Learningcited by 765
  5. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  6. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  7. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  8. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  9. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 334
  10. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 329
  11. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  12. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
14 more
  1. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  2. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 243
  3. 2020Blind Backdoors in Deep Learning Modelscited by 182
  4. 2018Mitigating Sybils in Federated Learning Poisoningcited by 161
  5. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 124
  6. 2018Blockchained On-Device Federated Learningcited by 106
  7. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 84
  8. 2019Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systemscited by 78
  9. 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 77
  10. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  11. 2019Split Learning for collaborative deep learning in healthcarecited by 73
  12. 2018Model-Reuse Attacks on Deep Learning Systemscited by 49
  13. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 29
  14. 2018Joint Service Pricing and Cooperative Relay Communication for Federated Learningcited by 22

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 4; 2024: 2; 2025: 3; 2026: 2.

15 papers.

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  1. 2022Feature Distribution Matching for Federated Domain Generalizationcited by 18
  2. 2022Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysiscited by 9
  3. 2026A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Designcited by 5
  4. 2022Bidirectional Contrastive Split Learning for Visual Question Answeringcited by 4
  5. 2023Instance-Level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Spacecited by 3
  6. 2024DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Modelscited by 3
  7. 2025Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontierscited by 3
  8. 2021Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detectioncited by 2
  9. 2023Mitigating Cross-client GANs-based Attack in Federated Learningcited by 2
  10. 2024Swarm Learning: A Survey of Concepts, Applications, and Trendscited by 2
  11. 2025Learning to Help in Multi-Class Settingscited by 2
  12. 2025Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Networkcited by 2
3 more
  1. 2023Meta Learning in Decentralized Neural Networks: Towards More General AIcited by 1
  2. 2023Meta Neural Coordinationcited by 0
  3. 2026Event-Triggered Gossip for Distributed Learningcited by 0