mlsanityData to 14 Oct 2026

Sparse Decentralized Federated Learning

arXiv 2308.16671 ↗First posted 31 Aug 2023cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

Decentralized Federated Learning (DFL) enables collaborative model training without a central server but faces challenges in efficiency, stability, and trustworthiness due to communication and computational limitations among distributed nodes. To address these critical issues, we introduce a sparsity constraint on the shared model, leading to Sparse DFL (SDFL), and propose a novel algorithm, CEPS. The sparsity constraint facilitates the use of one-bit compressive sensing to transmit one-bit information between partially selected neighbour nodes at specific steps, thereby significantly improving communication efficiency. Moreover, we integrate differential privacy into the algorithm to ensure privacy preservation and bolster the trustworthiness of the learning process. Furthermore, CEPS is underpinned by theoretical guarantees regarding both convergence and privacy. Numerical experiments validate the effectiveness of the proposed algorithm in improving communication and computation efficiency while maintaining a high level of trustworthiness.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 41 of its 69 references that are in the map. First posted on arXiv 31 Aug 2023.

Builds on

41 of 69 references.

References · 41

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2019Deep Leakage from Gradientscited by 1,021
  7. 2017Renyi Differential Privacycited by 650
  8. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  9. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 502
  10. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 468
  11. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 278
  12. 2019Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communicationcited by 221
29 more
  1. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  2. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
  3. 2021Decentralized Federated Averagingcited by 114
  4. 2019Decentralized Deep Learning with Arbitrary Communication Compressioncited by 111
  5. 2016Asynchronous Stochastic Gradient Descent with Delay Compensationcited by 99
  6. 2018Communication Compression for Decentralized Trainingcited by 88
  7. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  8. 2013Distributed optimization over time-varying directed graphscited by 78
  9. 2020Local and Central Differential Privacy for Robustness and Privacy in Federated Learningcited by 78
  10. 2022DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Trainingcited by 74
  11. 2019MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Samplingcited by 67
  12. 2021Decentralized Federated Learning through Proxy Model Sharingcited by 57
  13. 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
  14. 2021Decentralized Federated Learning: Balancing Communication and Computing Costscited by 55
  15. 2023Improving the Model Consistency of Decentralized Federated Learningcited by 45
  16. 2022Federated Learning via Inexact ADMMcited by 44
  17. 2020Decentralized Federated Learning via Mutual Knowledge Transfercited by 43
  18. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  19. 2023From Robustness to Privacy and Backcited by 27
  20. 2021IPLS : A Framework for Decentralized Federated Learningcited by 26
  21. 2022Tailoring Gradient Methods for Differentially-Private Distributed Optimizationcited by 18
  22. 20211-Bit Compressive Sensing for Efficient Federated Learning Over the Aircited by 11
  23. 2021Differentially Private Federated Learning via Inexact ADMMcited by 11
  24. 2022Decentralized Nonconvex Optimization with Guaranteed Privacy and Accuracycited by 10
  25. 2019Global and Quadratic Convergence of Newton Hard-Thresholding Pursuitcited by 9
  26. 2022FedGiA: An Efficient Hybrid Algorithm for Federated Learningcited by 8
  27. 2022Federated Stochastic Primal-dual Learning with Differential Privacycited by 5
  28. 2021Computing One-bit Compressive Sensing via Double-Sparsity Constrained Optimizationcited by 3
  29. 2015Pinball Loss Minimization for One-bit Compressive Sensing: Convex Models and Algorithmscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Decentralized Federated Learning by Partial Message Exchangecited by 1
  2. 2026Event-Triggered Gossip for Distributed Learningcited by 0