mlsanityData to 7 Oct 2026

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

arXiv 2205.07855 ↗First posted 7 May 2022cs.LG cs.AI cs.DCcited by 15

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Abstract (arXiv)

The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mobile and edge devices without data leaving the respective device, ensuring privacy by design. Yet, in order to scale this new paradigm beyond small groups of already entrusted entities towards mass adoption, the Federated Learning Framework (FLF) has to become (i) truly decentralized and (ii) participants have to be incentivized. This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning. 422 publications were retrieved, by querying 12 major scientific databases. Finally, 40 articles remained after a systematic review and filtering process for in-depth examination. Although having massive potential to direct the future of a more distributed and secure AI, none of the analyzed FLF is production-ready. The approaches vary heavily in terms of use-cases, system design, solved issues and thoroughness. We are the first to provide a systematic approach to classify and quantify differences between FLF, exposing limitations of current works and derive future directions for research in this novel domain.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 33 of its 120 references that are in the map. First posted on arXiv 7 May 2022.

Builds on

33 of 120 references.

References · 33

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  6. 2018LEAF: A Benchmark for Federated Settingscited by 780
  7. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  8. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 279
  9. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 178
  10. 2018Mitigating Sybils in Federated Learning Poisoningcited by 158
  11. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  12. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
21 more
  1. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 84
  2. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  3. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 75
  4. 2014IPFS - Content Addressed, Versioned, P2P File Systemcited by 67
  5. 2018Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchainscited by 48
  6. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  7. 2020FedCoin: A Peer-to-Peer Payment System for Federated Learningcited by 39
  8. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  9. 2020Federated Learning for 6G Communications: Challenges, Methods, and Future Directionscited by 32
  10. 2019Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithmcited by 26
  11. 2020Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Managementcited by 26
  12. 2020BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecturecited by 20
  13. 2018Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learningcited by 17
  14. 2021Transparent Contribution Evaluation for Secure Federated Learning on Blockchaincited by 16
  15. 2021A Systematic Survey of Blockchained Federated Learningcited by 15
  16. 2020Estimation of Individual Device Contributions for Incentivizing Federated Learningcited by 14
  17. 2020Incentives for Federated Learning: a Hypothesis Elicitation Approachcited by 12
  18. 2021Data-Free Evaluation of User Contributions in Federated Learningcited by 10
  19. 2021Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensingcited by 6
  20. 2021Reward-Based 1-bit Compressed Federated Distillation on Blockchaincited by 2
  21. 2021uTango: an open-source TEE for IoT devicescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 4; 2025: 1; 2026: 2.

15 papers.

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  1. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2023Towards Quantum Federated Learningcited by 20
  4. 2023Smart Policy Control for Securing Federated Learning Management Systemcited by 8
  5. 2024SoK: Blockchain-Based Decentralized AI (DeAI)cited by 6
  6. 2023Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computingcited by 4
  7. 2023A Systematic Literature Review on Client Selection in Federated Learningcited by 3
  8. 2023A Framework for Incentivized Collaborative Learningcited by 2
  9. 2024Blockchain and Artificial Intelligence: Synergies and Conflictscited by 2
  10. 2024Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learningcited by 2
  11. 2024Structured Reinforcement Learning for Incentivized Stochastic Covert Optimizationcited by 1
  12. 2026Centralized vs Decentralized Federated Learning: A trade-off performance analysiscited by 1
3 more
  1. 2023Scalable Data Point Valuation in Decentralized Learningcited by 0
  2. 2025Strategic Incentivization for Locally Differentially Private Federated Learningcited by 0
  3. 2026Knowledge-Free Correlated Agreement for Incentivizing Federated Learningcited by 0