Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

Cited by (188)

Items 101–188 of 188

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2. FedLG: Lightweight Generic Certificateless Authentication for Trustworthy Federated Learning in VANETs
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3. Privacy–Preserving Online Content Moderation: A Federated Learning Use Case
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4. Gradient Inversion Attacks on Acoustic Signals: Revealing Security Risks in Audio Recognition Systems
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6. IMFL: An Incentive Mechanism for Federated Learning With Personalized Protection
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7. Robust Estimation Method against Poisoning Attacks for Key-Value Data with Local Differential Privacy
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8. GTV: Generating Tabular Data via Vertical Federated Learning
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9. Fed-CAD: Federated Learning with Correlation-aware Adaptive Local Differential Privacy
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10. Efficient and Secure Federated Knowledge Transfer Under Non-IID Settings in IoT
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11. Federated Learning with Differential Privacy and an Untrusted Aggregator
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12. Differential Privacy Enabled Robust Asynchronous Federated Multitask Learning: A Multigradient Descent Approach
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13. A privacy-enhancing federated learning framework for cross-manufacturer LPBF powder bed defect identification
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14. Incentivized Federated Learning with Local Differential Privacy Using Permissioned Blockchains
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15. Towards Understanding the Risks of Gradient Inversion in Federated Learning
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16. Secure and Privacy-preserving Federated Learning with Explainable Artificial Intelligence for Smart Healthcare System
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17. Mitigation of Membership Inference Attack by Knowledge Distillation on Federated Learning
    Rei UEDA, Tsunato Nakai, Kota Yoshida, Takeshi Fujino · 2024 · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences
18. Personalized Differential Privacy Federated Learning Model for Internet of Vehicles
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19. Poisoning as a Post-Protection: Mitigating Membership Privacy Leakage From Gradient and Prediction of Federated Models
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20. Hierarchical Selection-Based Local Differential Privacy Federated Learning for Distributed Robotic Vehicle Collaboration with 6G
    Guoyue Cai, Weidong Zhang, Wei Zhao, Xuangou Wu · 2025 · IEEE International Conference on Communications workshops
21. Breaking the Illusion: A Critical Study of Backdoor Defense in Federated Learning With Non-IID Data
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22. Differentially-Private Multi-Tier Federated Learning: A Formal Analysis and Evaluation
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23. Privacy-Preserving Online Content Moderation with Federated Learning
    Pantelitsa Leonidou, Nicolas Kourtellis, Nikos Salamanos, Michael Sirivianos · 2023 · International World Wide Web Conference (WWW)
24. Privacy-Preserving Federated Learning for Healthcare Data
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25. Advancing Edge Artificial Intelligence
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26. ATELIER: Service Tailored and Limited-Trust Network Analytics Using Cooperative Learning
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27. Federated Bilevel Learning Against Model Poisoning Attacks
    Yuan Yuan, Yingfan Deng, Xiao Zhang, Yifei Zou, Yangguang Shi, Dongxiao Yu · 2025 · IEEE Transactions on Networking
28. FedDeepInsight—A privacy-first federated learning architecture for medical data
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29. SEMMI: Multi-party security decision-making scheme for linear functions in the internet of medical things
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30. Advancing Edge Artificial Intelligence
    Ovidiu Vermesan, Dave Marples · 2023
31. Analysing Utility Loss in Federated Learning with Differential Privacy
    Anastasia Pustozerova, Jan Baumbach, Rudolf L. Mayer · 2023 · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
32. Types of Attacks on Federated Neural Networks and Methods of Protection
    V. A. Kostenko, Alisa Evgenievna Selezneva · 2024 · Proceedings of the Institute for System Programming of RAS
33. Privacy Leakage in Federated Learning for Smart Grid Short-Term Load Forecasting
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34. Efficient Byzantine-Robust Federated Learning Based on the Multimessage Shuffle Protocol for Consumer Internet of Things
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35. HeaPS: Heterogeneity-aware participant selection for efficient federated learning
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36. DSPFL: A Deep-Layer Sign Sharing Personalized Federated Learning Scheme for Mitigating Poisoning Attacks
    Chenhao Xu, Nasrin Sohrabi, Youyang Qu, Hai Dong, Zahir Tari, Xun Yi · 2025 · IEEE Transactions on Neural Networks and Learning Systems
37. Avaliação da Privacidade Diferencial Aplicada ao Aprendizado Federado Através de Ataques de Inversão de Modelo
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38. Toward Double-Layer Data Privacy in Communication-Efficient Hierarchical Federated Learning: A Client Sampling Approach
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39. DVFL: A Lightweight Verifiable Federated Learning System With Dynamic Obfuscation for Resource-Constrained IoT Devices
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40. Dynamic Secure Multi Broad Network for Privacy Preserving of Streaming Data
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41. FL-HSDP: Client-Side Defense for Federated Learning via Hessian Optimization and Shuffled Differential Privacy
    Yuting Li, Guojun Wang, Tian Wang, Weijia Jia · 2023 · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
42. DPC: Filtering Out Patch-Based Poisoned Samples with Differential Privacy
    Yukun Yan, Peng Tang, Rui Chen, Qilong Han, Ruochen Du · 2024 · Lecture notes in computer science
43. PR-PFL: A Privacy-Preserving and Robust Personalized Federated Learning Framework
    Ruiguang Yang, Xiaodong Shen, Chang Liang Xu, Liehuang Zhu, Kashif Sharif · 2024 · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)
44. A Privacy-Fidelity Tradeoff Framework in Post-Processed Machine Learning
    Md. Faisal Ahmed, Zhengdao Wang · 2025 · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)
45. Towards Understanding the Risks of Gradient Inversion in Federated Learning
    Ali Hatamizadeh, Hongxu Yin, Pavlo A. Molchanov, Andriy Myronenko, Wenqi Li, Prerna Dogra, Andrew Feng, Mona G. Flores, Jan Kautz, Daguang Xu et al. · 2021 · Research Square
46. Exploring the Privacy-Utility Tradeoff in Differentially Private Federated Learning for Mobile Health: A Novel Approach using Simulated Privacy Attacks
    Alexander Shen, Luke Francisco, Srijan Sen, Ambuj Tewari · 2022 · medRxiv
47. Evaluation of Membership Inference Attack Against Federated Learning With Differential Privacy on Edge Devices
    Rei Ueda, Tsunato Nakai, Kota Yoshida, Takeshi Fujino · 2023 · IEEE Global Conference on Consumer Electronics (GCCE)
48. Federated Learning: Privacy, Security and Hardware Perspectives
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49. Basalt: Server-Client Joint Defense Mechanism for Byzantine-Robust Federated Learning
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50. Differential Privacy with Selected Privacy Budget $$\epsilon $$ in a Cyber Physical System Using Machine Learning
    Ruilin Wang, Chuadhry Mujeeb Ahmed · 2024 · Lecture notes in computer science
51. Does Differential Privacy Prevent Backdoor Attacks in Practice?
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52. Recent Advances in Federated Graph Learning
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53. FedDeepInsight - A Privacy-First Federated Learning Architecture for Medical Data
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54. Adaptive Differential Privacy Based Optimization Scheme for Federated Learning
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55. Hypnopaedia-Aware Machine Unlearning via Psychometrics of Artificial Mental Imagery
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56. A Selective Privacy-Preserving Federal Learning Framework
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57. Privacy-Preserving Mitigation of Poisoning Attacks in Three-Tier Federated Learning
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58. A gradient inversion attack defense method based on data augmentation
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59. Types of Attacks against Federated Neural Networks and Protection Methods
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60. Enhancing Privacy and Robustness in Federated Learning with Local Data Distribution Invariance and Byzantine-Resilient Aggregation
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61. Coarse-Grained Dynamic Differential Privacy Federated Learning based on D3QN
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62. SDFL: a privacy-preserving Byzantine-robust decentralized federated learning scheme
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63. Secure Rational Delegation Federated Learning
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64. Discrete Gaussian Integer Aggregation and Trust-Budget Gating for Federated Learning in IoT-Enabled CPS
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65. Privacy-Preserving Federated Evolutionary Optimization with Personalized Data Transformation
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66. A Robust Federated Learning Framework Integrating Principal Component Analysis and Dual Detection Mechanisms
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67. Causal Inference-Based Collaborative Membership Inference Attack on Federated Recommendation
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68. Fidelity-Optimizing Defense Mechanism Against Membership Inference Attacks
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69. NeuroScope: Trigger-Induced Activation Analysis for Backdoor Detection in Federated Learning
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70. Adaptive privacy-preserving federated learning for robust IoT systems: A defense against data poisoning attacks
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71. GAFIN: Gradient Direction-Agnostic Differential Privacy Control for Federated Learning via Nussbaum-Enhanced PID Tracking
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72. Applications of secure multi-party computation in Machine Learning
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73. DFMAD: Data-Free Backdoor Defense for Federated Learning via Multi-Teacher Adversarial Distillation
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74. Sibai: A Few-Shot Meta-Classifier for Poisoning Detection in Federated Learning
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75. Privacy-Preserving Federated Learning with Knowledge Distillation for Heterogeneous IoT Nodes
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76. BadPromptFL: Parameters-Free Backdoor Attack on Vision-Language Federated Learning
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77. FedHCG: A Byzantine Robust Federated Learning Framework With Hierarchical Confidence Estimation and Geometric Consistency
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78. A comprehensive survey on communication optimization and privacy preservation in federated learning
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79. Less is More: Persistent Low-Frequency Backdoor Injection in Federated Learning
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80. Effects of Data Heterogeneity and Differential Privacy on Federated Learning
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81. FedSC: Backdoor Defense in Federated Learning via Model Splitting and Benign Consensus Clustering
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82. SpecGate: Spectral Decomposition and LOF-Gated Aggregation for Defending Against Backdoor Attacks in Federated Learning
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83. Fortress: Multi-Level Secure and Efficient Distributed Learning
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84. PREFed: An Effective and Stealthy Static-Anchor Backdoor Attack via Trigger Pre-Optimization in Federated Learning
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85. Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
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86. Privacy Preserving Federated Learning for Insider Threat Detection
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87. SNR-Guided Model Sizing for Differentially Private Federated Learning with LiRA Privacy Auditing
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88. BlockFedIDS: A Blockchain-Enabled Federated Learning Framework for Privacy-Preserving Intrusion Detection in Heterogeneous IoT Networks
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