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A Secure Federated Learning Framework for 5G Networks

arXiv 2005.05752 ↗First posted 12 May 2020cs.CR cs.LG cs.NIcited by 32

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Abstract (arXiv)

Federated Learning (FL) has been recently proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global models or privacy leakage of FL models. Therefore, it is crucial for FL to develop security means of defense. In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from involving in FL. In doing so, the central aggregator recognizes malicious and unreliable participants by automatically executing smart contracts to defend against poisoning attacks. Further, we use local differential privacy techniques to prevent membership inference attacks. Numerical results suggest that the proposed framework can effectively deter poisoning and membership inference attacks, thereby improving the security of FL in 5G networks.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2020.

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Citing papers per year: 2021: 9; 2022: 9; 2023: 5; 2024: 6; 2025: 2; 2026: 1.

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  1. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
  2. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 55
  3. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  4. 2021FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumoniacited by 22
  5. 2024Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baselinecited by 17
  6. 2022Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoTcited by 15
  7. 2023IronForge: An Open, Secure, Fair, Decentralized Federated Learningcited by 15
  8. 2021Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensuscited by 14
  9. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  10. 2022Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Dronescited by 7
  11. 2022Federated Reinforcement Learning for Collective Navigation of Robotic Swarmscited by 7
  12. 2022Federated Learning for Distributed Spectrum Sensing in NextG Communication Networkscited by 7
20 more
  1. 2022A Practical Cross-Device Federated Learning Framework over 5G Networkscited by 7
  2. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  3. 2022Jamming Attacks on Federated Learning in Wireless Networkscited by 6
  4. 2022Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networkscited by 6
  5. 2023Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semanticscited by 5
  6. 2024Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutionscited by 5
  7. 2023FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attackscited by 4
  8. 2023Trustworthy Privacy-preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 with Blockchaincited by 3
  9. 2021Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehiclescited by 2
  10. 2021Analysis and Evaluation of Synchronous and Asynchronous FLchaincited by 2
  11. 2021Robust Convergence in Federated Learning through Label-wise Clusteringcited by 2
  12. 2023Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solutioncited by 2
  13. 2024Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergencecited by 1
  14. 2026Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluationcited by 1
  15. 2022Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Datacited by 0
  16. 2022Inverse Feasibility in Over-the-Air Federated Learningcited by 0
  17. 2024Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizationscited by 0
  18. 2024Optimisation of federated learning settings under statistical heterogeneity variationscited by 0
  19. 2025AugMixCloak: A Defense against Membership Inference Attacks via Image Transformationcited by 0
  20. 2025SEMFED: Semantic-Aware Resource-Efficient Federated Learning for Heterogeneous NLP Taskscited by 0