mlsanityData to 7 Oct 2026

Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent

arXiv 2104.08100 ↗First posted 15 Apr 2021cs.LG cs.AIcited by 5

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Abstract (arXiv)

By leveraging deep learning based technologies, the data-driven based approaches have reached great success with the rapid increase of data generated of Industrial Indernet of Things(IIot). However, security and privacy concerns are obstacles for data providers in many sensitive data-driven industrial scenarios, such as healthcare and auto-driving. Many Federated Learning(FL) approaches have been proposed with DNNs for IIoT applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topogy based decentralized federated learning(RDFL) scheme for Deep Generative Models(DGMs), where DGMs is a promising solution for solving the aforementioned data usability issues. Compare with existing IIoT FL works, our RDFL schemes provides communication efficiency and maintain training performance to boost DGMs in target IIoT tasks. A novel ring FL topology as well as a map-reduce based synchronizing method are designed in the proposed RDFL to improve decentralized FL performance and bandwidth utilization. In addition, InterPlanetary File System(IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstate the superiority of RDFL with either independent and identically distributed(IID) datasets or non-independent and identically distributed(Non-IID) datasets.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 27 of its 37 references that are in the map. First posted on arXiv 15 Apr 2021.

Builds on

27 of 37 references.

References · 27

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2018Federated Learning with Non-IID Datacited by 1,099
  4. 2018LEAF: A Benchmark for Federated Settingscited by 780
  5. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  6. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  7. 2019Federated Machine Learning: Concept and Applicationscited by 409
  8. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  9. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  10. 2020Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Datacited by 125
  11. 2019Decentralized Deep Learning with Arbitrary Communication Compressioncited by 109
  12. 2018Blockchained On-Device Federated Learningcited by 105
15 more
  1. 2018A generic framework for privacy preserving deep learningcited by 103
  2. 2020FedGAN: Federated Generative Adversarial Networks for Distributed Datacited by 89
  3. 2018Communication Compression for Decentralized Trainingcited by 87
  4. 2019Generative Models for Effective ML on Private, Decentralized Datasetscited by 85
  5. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 83
  6. 2019Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networkscited by 79
  7. 2014IPFS - Content Addressed, Versioned, P2P File Systemcited by 67
  8. 2020Federated Learning With Quantized Global Model Updatescited by 54
  9. 2019Central Server Free Federated Learning over Single-sided Trust Social Networkscited by 33
  10. 2020Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selectioncited by 20
  11. 2020Federated Generative Adversarial Learningcited by 19
  12. 2021Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversarycited by 14
  13. 2019Privacy-Preserving Blockchain-Based Federated Learning for IoT Devicescited by 12
  14. 2019Decentralized Learning of Generative Adversarial Networks from Non-iid Datacited by 10
  15. 2014A Fast, Minimal Memory, Consistent Hash Algorithmcited by 2

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Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 1.

5 papers.

  1. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  2. 2023Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyondcited by 14
  3. 2022Straggler-Resilient Differentially-Private Decentralized Learningcited by 3
  4. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  5. 2022Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenariocited by 2