mlsanityData to 7 Oct 2026

Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges

arXiv 2104.01776 ↗First posted 5 Apr 2021cs.CR eess.SPcited by 75

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Abstract (arXiv)

Mobile edge computing (MEC) has been envisioned as a promising paradigm to handle the massive volume of data generated from ubiquitous mobile devices for enabling intelligent services with the help of artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training in a single entity, e.g., an MEC server, which is now becoming a weak point due to data privacy concerns and high data communication overheads. In this context, federated learning (FL) has been proposed to provide collaborative data training solutions, by coordinating multiple mobile devices to train a shared AI model without exposing their data, which enjoys considerable privacy enhancement. To improve the security and scalability of FL implementation, blockchain as a ledger technology is attractive for realizing decentralized FL training without the need for any central server. Particularly, the integration of FL and blockchain leads to a new paradigm, called FLchain, which potentially transforms intelligent MEC networks into decentralized, secure, and privacy-enhancing systems. This article presents an overview of the fundamental concepts and explores the opportunities of FLchain in MEC networks. We identify several main topics in FLchain design, including communication cost, resource allocation, incentive mechanism, security and privacy protection. The key solutions for FLchain design are provided, and the lessons learned as well as the outlooks are also discussed. Then, we investigate the applications of FLchain in popular MEC domains, such as edge data sharing, edge content caching and edge crowdsensing. Finally, important research challenges and future directions are also highlighted.

Key facts. Cited by 75 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Apr 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 20; 2023: 16; 2024: 16; 2025: 13; 2026: 3.

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  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 81
  4. 2021Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advancescited by 54
  5. 2022Gradient Obfuscation Gives a False Sense of Security in Federated Learningcited by 37
  6. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  7. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  8. 2022The Cost of Training Machine Learning Models over Distributed Data Sourcescited by 18
  9. 2022Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Reviewcited by 15
  10. 2022Trustworthy Federated Learning via Blockchaincited by 15
  11. 2023IronForge: An Open, Secure, Fair, Decentralized Federated Learningcited by 15
  12. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
38 more
  1. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
  2. 2022Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computingcited by 13
  3. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  4. 2022HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networkscited by 12
  5. 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
  6. 2024Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimizationcited by 12
  7. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  8. 2023ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?cited by 9
  9. 2024Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Surveycited by 9
  10. 2023Smart Policy Control for Securing Federated Learning Management Systemcited by 8
  11. 2023Defending Against Poisoning Attacks in Federated Learning with Blockchaincited by 8
  12. 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 8
  13. 2024Privacy-Preserving in Blockchain-based Federated Learning Systemscited by 7
  14. 2024Byzantine-Robust Aggregation for Securing Decentralized Federated Learningcited by 7
  15. 2021Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trendscited by 6
  16. 2021Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensingcited by 6
  17. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  18. 2022A Fast Blockchain-based Federated Learning Framework with Compressed Communicationscited by 5
  19. 2023Blockchained Federated Learning for Internet of Things: A Comprehensive Surveycited by 5
  20. 2022A Comprehensive Survey on Federated Learning: Concept and Applicationscited by 4
  21. 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
  22. 2022FAIR-BFL: Flexible and Incentive Redesign for Blockchain-based Federated Learningcited by 4
  23. 2022Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networkscited by 4
  24. 2022ModularFed: Leveraging Modularity in Federated Learning Frameworkscited by 4
  25. 2023Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computingcited by 4
  26. 2023Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verificationcited by 4
  27. 2024Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approachcited by 4
  28. 2024Digital Twin in Industries: A Comprehensive Surveycited by 4
  29. 2025VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learningcited by 4
  30. 2021DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topologycited by 3
  31. 2022On the Decentralization of Blockchain-enabled Asynchronous Federated Learningcited by 3
  32. 2023A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computingcited by 3
  33. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  34. 2021Analysis and Evaluation of Synchronous and Asynchronous FLchaincited by 2
  35. 2022Cross-Silo Heterogeneous Model Federated Multitask Learningcited by 2
  36. 2022Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directionscited by 2
  37. 2023Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learningcited by 2
  38. 2024AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated Learningcited by 2

50 most cited of 75 All 75 (JSON)