mlsanityData to 7 Oct 2026

On Safeguarding Privacy and Security in the Framework of Federated Learning

arXiv 1909.06512 ↗First posted 14 Sep 2019cs.NIcited by 55

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Abstract (arXiv)

Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL). Specifically, FL allows a decoupling of data provision at UEs and ML model aggregation at a central unit. By training model locally, FL is capable of avoiding data leakage from the UEs, thereby preserving privacy and security to some extend. However, even if raw data are not disclosed from UEs, individual's private information can still be extracted by some recently discovered attacks in the FL architecture. In this work, we analyze the privacy and security issues in FL, and raise several challenges on preserving privacy and security when designing FL systems. In addition, we provide extensive simulation results to illustrate the discussed issues and possible solutions.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Sep 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 8; 2023: 16; 2024: 11; 2025: 4; 2026: 3.

55 papers.

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  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  2. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  3. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  4. 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
  5. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 55
  6. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  7. 2021Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filteringcited by 28
  8. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  9. 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 24
  10. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  11. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  12. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
38 more
  1. 2021Federated Learning from Small Datasetscited by 14
  2. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
  3. 2022Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computingcited by 13
  4. 2023Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learningcited by 13
  5. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  6. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  7. 2022Federated learning: Applications, challenges and future directionscited by 11
  8. 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
  9. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  10. 2021Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Designcited by 9
  11. 2023G2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clusteringcited by 9
  12. 2024One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversitycited by 8
  13. 2021Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimizationcited by 6
  14. 2022When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challengescited by 6
  15. 2022Federated Stochastic Primal-dual Learning with Differential Privacycited by 5
  16. 2023Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learningcited by 5
  17. 2023Secure Federated Learning for Cognitive Radio Sensingcited by 4
  18. 2023Differentially Private Multi-Site Treatment Effect Estimationcited by 4
  19. 2023MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learningcited by 4
  20. 2023Federated Learning Based Multilingual Emoji Prediction In Clean and Attack Scenarioscited by 3
  21. 2023Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacycited by 3
  22. 2023Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Surveycited by 3
  23. 2024Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memorycited by 3
  24. 2021Utility Fairness for the Differentially Private Federated Learningcited by 2
  25. 2021Robust Convergence in Federated Learning through Label-wise Clusteringcited by 2
  26. 2024Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approachcited by 2
  27. 2024Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learningcited by 2
  28. 2025pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixupcited by 2
  29. 2022Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learningcited by 1
  30. 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequencescited by 1
  31. 2023Edge AI for Internet of Energy: Challenges and Perspectivescited by 1
  32. 2024Interventional Causal Structure Discovery over Graphical Models with Convergence and Optimality Guaranteescited by 1
  33. 2024A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applicationscited by 1
  34. 2026Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacycited by 1
  35. 2022Inverse Feasibility in Over-the-Air Federated Learningcited by 0
  36. 2023Applications of Federated Learning in IoT for Hyper Personalisationcited by 0
  37. 2023Secure Decentralized Learning with Blockchaincited by 0
  38. 2024Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizationscited by 0

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