Intrusion detection system based on federated learning using layer-based privacy obfuscation (original) (raw)

References

  1. Shoukat, S., Gao, T., Javeed, D., Saeed, M.S., Adil, M.: Trust my IDS: An explainable AI integrated deep learning-based transparent threat detection system for industrial networks. Comput. Secur. 149, 104191 (2025)
    Article Google Scholar
  2. Awasthi, A., Krpalkova, L., Walsh, J.: Bridging the maturity gaps in industrial data science: navigating challenges in IoT-driven manufacturing. Technologies 13(1), 22 (2025)
    Article Google Scholar
  3. Khan, B.U.I., Goh, K.W., Mir, M.S., Mohd Rosely, N.F.L., Mir, A.A., Chaimanee, M.: Blockchain-enhanced sensor-as-a-service (SEaaS) in IoT: Leveraging blockchain for efficient and secure sensing data transactions. Information 15, 212 (2024)
    Article Google Scholar
  4. Khan, B.U.I., Goh, K.W., Khan, A.R., Zuhairi, M.F., Chaimanee, M.: Integrating AI and Blockchain for enhanced data security in IoT-driven smart cities. Processes (2024). https://doi.org/10.3390/pr12091825
    Article Google Scholar
  5. Deebak, B.D., Hwang, S.O.: Privacy-preserving learning model using lightweight encryption for visual sensing industrial IoT devices. IEEE Trans. Emerg. Topics Comput. Intell. (2025). https://doi.org/10.1109/TETCI.2024.3523771
    Article Google Scholar
  6. N. Anjum, Z. Latif, and H. Chen, 2025 Security and privacy of industrial big data: Motivation, opportunities, and challenges. Journal of Network and Computer Applications. 104130
  7. Cai, Z., Chen, J., Fan, Y., Zheng, Z., Li, K.: Blockchain-empowered federated learning: benefits, challenges, and solutions. IEEE Trans. Big Data (2025). https://doi.org/10.1109/TBDATA.2025.3541560
    Article Google Scholar
  8. Fu, C., Chen, H., Ruan, N.: Privacy for free: spy attack in vertical federated learning by both active and passive parties. IEEE Trans. Inform. Forens. Secur. (2025). https://doi.org/10.1109/TIFS.2025.3534469
    Article Google Scholar
  9. Roy, A., Mahanta, D.R., Mahanta, L.B.: A semi-synchronous federated learning framework with chaos-based encryption for enhanced security in medical image sharing. Res. Eng. 25, 103886 (2025)
    Google Scholar
  10. Wang, S., Gai, K., Yu, J., Zhang, Z., Zhu, L.: PravFed: practical heterogeneous vertical federated learning via representation learning. IEEE Trans. Inform. Forens. Secur. (2025). https://doi.org/10.1109/TIFS.2025.3530700
    Article Google Scholar
  11. M. Arazzi, S. Nicolazzo, and A. Nocera, "A defense mechanism against label inference attacks in vertical federated learning," Neurocomputing, p. 129476, 2025.
  12. Li, Z., Zhang, Y.: Advancing membership inference attacks: the present and the future. Secur. Safety 4, 2024017 (2025)
    Article Google Scholar
  13. Li, Z., Bao, H., Pan, H., Guan, M., Huang, C., Dai, H.N.: UEFL: universal and efficient privacy-preserving federated learning. IEEE Int. Things J. (2025). https://doi.org/10.1109/JIOT.2025.3525731
    Article Google Scholar
  14. Cao, S., Liu, S., Yang, Y., Du, W., Zhan, Z., Wang, D., Zhang, W.: A hybrid and efficient federated learning for privacy preservation in IoT devices. Ad Hoc Netw. 170, 103761 (2025)
    Article Google Scholar
  15. Kong, X., He, X., Ma, X., Yan, X., Wang, L., Shen, G., Liu, Z.: Oh-FedRec: one-shot and heterogeneous vertical federated recommendation system. IEEE Trans. Consum. Electron. (2025). https://doi.org/10.1109/TCE.2025.3532724
    Article Google Scholar
  16. Zhou, J., Wu, J., Ni, J., Wang, Y., Pan, Y., Su, Z.: Protecting your attention during distributed graph learning: Efficient privacy-preserving federated graph attention network. IEEE Trans. Inform. Forens. Secur. (2025). https://doi.org/10.1109/TIFS.2025.3536612
    Article Google Scholar
  17. Asad, M., Otoum, S.: BPPFL: a blockchain-based framework for privacy-preserving federated learning. Clust. Comput. 28(2), 126 (2025)
    Article Google Scholar
  18. Chen, J., Jiang, H., Hu, Q.: Utility-enhanced personalized privacy preservation in hierarchical federated learning. IEEE Trans. Mob. Comput. (2025). https://doi.org/10.1109/TMC.2025.3531919
    Article Google Scholar
  19. Báskay, J., Mezei, T., Banczerowski, P., Horváth, A., Joó, T., Pollner, P.: Censoring sensitivity analysis for benchmarking survival machine learning methods. Sci 7(1), 18 (2025)
    Article Google Scholar
  20. Latif, N., Ma, W., Ahmad, H.B.: Advancements in securing federated learning with IDS: a comprehensive review of neural networks and feature engineering techniques for malicious client detection. Artif. Intell. Rev. 58(3), 91 (2025)
    Article Google Scholar
  21. Chen, G., Wang, W., Wu, Y., Li, C., Xu, G., Ji, S., Han, Y.: RobustPFL: robust personalized federated learning. IEEE Trans. Depend. Secure Comput. (2025). https://doi.org/10.1109/TDSC.2025.3526840
    Article Google Scholar
  22. Khan, B.U.I., Anwar, F., Olanrewaju, R.F., Pampori, B.R., Mir, R.N.: A novel multi-agent and multilayered game formulation for intrusion detection in Internet of Things (IoT). IEEE Access 8, 98481–98490 (2020)
    Article Google Scholar
  23. Sowmya, T.: A novel stable feature selection algorithm for machine learning-based intrusion detection system. Procedia Comput. Sci. 252, 738–747 (2025)
    Article Google Scholar
  24. Ahmed, U., Nazir, M., Sarwar, A., Ali, T., Aggoune, E.H.M., Shahzad, T., Khan, M.A.: Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering. Sci. Rep. 15(1), 1726 (2025)
    Article Google Scholar
  25. Ouyang, J., Han, R., Zuo, X., Cheng, Y., Liu, C.H.: Accuracy-aware differential privacy in federated learning of large transformer models. J. Inform. Secur. Appl. 89, 103986 (2025)
    Google Scholar
  26. Zhang, B., Mao, Y., He, X., Ping, P., Huang, H., Wu, J.: Exploring the privacy-accuracy trade-off using adaptive gradient clipping in federated learning. IEEE Trans. Net. Sci. Eng. (2025). https://doi.org/10.1109/TNSE.2025.3546777
    Article Google Scholar
  27. Ali, W., Zhou, X., Shao, J.: Privacy-preserved and responsible recommenders: from conventional defense to federated learning and blockchain. ACM Comput. Surv. 57(5), 1–35 (2025)
    Article Google Scholar
  28. Dritsas, E., Trigka, M.: Federated learning for IoT: a survey of techniques, challenges, and applications. J. Sens. Actuator Netw. 14(1), 9 (2025)
    Article Google Scholar
  29. Alamer, A., Basudan, S.: A privacy-preserving federated learning with a feature of detecting forged and duplicated gradient model in autonomous vehicle. IEEE Access (2025). https://doi.org/10.1109/ACCESS.2025.3545786
    Article Google Scholar
  30. Alebouyeh, Z., Bidgoly, A.J.: Privacy-preserving federated learning compatible with robust aggregators. Eng. Appl. Artif. Intell. 143, 110078 (2025)
    Article Google Scholar
  31. Tian, S., Tan, Y., Wang, H., Liu, H., Li, Z.: ASDIA: an adversarial sample to preserve privacy program in federated learning. IEEE Trans. Depend. Secure Comput. (2025). https://doi.org/10.1109/TDSC.2025.3545599
    Article Google Scholar
  32. Yu, D., Zhang, H., Huang, Y., Xie, Z.: Data distribution inference attack in federated learning via reinforcement learning support. High-Conf. Comput. 5(1), 100235 (2025)
    Article Google Scholar
  33. Agarwal, V., Ardakanian, O., Pal, S.: Robust peer-to-peer federated learning for non-intrusive load monitoring in smart homes. Energy Build. 329, 115209 (2025)
    Article Google Scholar
  34. Aryavalli, S.N.G., Kumar, H.: Top 12 layer-wise security challenges and a secure architectural solution for Internet of Things. Comput. Electr. Eng. 105, 108487 (2023)
    Article Google Scholar
  35. Dhingra, D., Dua, M.: Novel multiple video encryption scheme using two-chaotic-map-based two-level permutation and diffusion. Nonlinear Dyn. (2025). https://doi.org/10.1007/s11071-024-10820-7
    Article Google Scholar
  36. Naresh, V.S.: PPDNN-CRP: privacy-preserving deep neural network processing for credit risk prediction in cloud: a homomorphic encryption-based approach. J. Cloud Comput. 13(1), 149 (2024)
    Article Google Scholar
  37. Pradeepthi, C., Maheswari, B.U.: Network intrusion detection and prevention strategy with data encryption using hybrid detection classifier. Multimed. Tools Appl. 83(13), 40147–40178 (2024)
    Article Google Scholar
  38. B. Yalavarthi, A. R. Kaushik, T. Sharma, C. Jutla, and N. Ratha, 2025 "Secure sleep apnea detection with FHE and deep learning on ECG signals," in International Conference on Pattern Recognition, Cham: Springer. pp. 49–64.
  39. Kokaj, A., Mollakuqe, E.: Mathematical proposal for securing split learning using homomorphic encryption and zero-knowledge proofs. Appl. Sci. 15(6), 2913 (2025)
    Article Google Scholar
  40. Deng, L., Li, L., Ou, Y., Xiang, J., Xia, S.: Tripm: a multi-label deep learning SCA model for multi-byte attacks. Int. J. Mach. Learn. Cybernet. (2025). https://doi.org/10.1007/s13042-025-02552-w
    Article Google Scholar
  41. Xhemrishi, M., Östman, J., Wachter-Zeh, A.: FedGT: identification of malicious clients in federated learning with secure aggregation. IEEE Trans. Inform. Forens. Secur. (2025). https://doi.org/10.1109/TIFS.2025.3539964
    Article Google Scholar
  42. Pan, H., Bao, H., Guan, M., Li, Z., Huang, C., Dai, H.N.: DualGuard: Obfuscated federated learning with two-party secure robust aggregation. IEEE Int. Things J. (2025). https://doi.org/10.1109/JIOT.2025.3533087
    Article Google Scholar
  43. Orabi, M.M., Emam, O., Fahmy, H.: Adapting security and decentralized knowledge enhancement in federated learning using blockchain technology: literature review. J. Big Data 12(1), 55 (2025)
    Article Google Scholar
  44. Gao, W., Ren, S., Liu, Z., Qin, B., Dong, X., Zhao, Z.: Lattice-based group signature with VLR for anonymous medical service evaluation system. Electronics 14(4), 680 (2025)
    Article Google Scholar
  45. Tiferes, R.R., Manassero, G., Pellini, E.L., di Santo, S.G.: Biweight midcorrelation based transmission line pilot protection algorithm. IEEE Open Access J. Power Energy 11, 68–82 (2024)
    Article Google Scholar
  46. Galindo-Hernández, R., Rodríguez-Vázquez, K., Galán-Vásquez, E., Hernández Castellanos, C.I.: Online-adjusted evolutionary biclustering algorithm to identify significant modules in gene expression data. Brief. Bioinfor. 26(1), bbae681 (2025)
    Article Google Scholar
  47. Liang, H., Yang, X., Han, X., Liu, B., Hu, C., Wang, D., Cheng, D.: Spread+: Scalable model aggregation in federated learning with non-IID data. IEEE Trans. Parallel Distrib. Syst. (2025). https://doi.org/10.1109/TPDS.2025.3539738
    Article Google Scholar
  48. Milan Kummaya, A., Joseph, A., Rajamani, K., Ghinea, G.: Fed-Hetero: a self-evaluating federated learning framework for data heterogeneity. Appl. Syst. Innovat. 8(2), 28 (2025)
    Article Google Scholar
  49. Zhou, W., Zhang, D., Wang, H., Li, J., Jiang, M.: A meta-reinforcement learning-based poisoning attack framework against federated learning. IEEE Access (2025). https://doi.org/10.1109/ACCESS.2025.3538891
    Article Google Scholar
  50. Hossain, M.T., Badsha, S., La, H., Islam, S., Khalil, I.: Exploiting Gaussian noise variance for dynamic differential poisoning in federated learning. IEEE Trans. Artif. Intell (2025). https://doi.org/10.1109/TAI.2025.3540030
    Article Google Scholar
  51. Huang, S., Li, Y., Yan, X., Gao, Y., Chen, C., Shi, L., Ng, W.W.: Scope: On detecting constrained backdoor attacks in federated learning. IEEE Trans. Inform. Foren. Secur. (2025). https://doi.org/10.1109/TIFS.2025.3533899
    Article Google Scholar
  52. A. A. Wardana, G. Kołaczek, and P. Sukarno, 2025 "CoAt-Set: Transformed coordinated attack dataset for collaborative intrusion detection simulation," Data in Brief, p. 111354
  53. H. M. Son, M. H. Kim, T. M. Chung, C. Huang, and X. Liu, 2024 FedUV: Uniformity and variance for heterogeneous federated learning, in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR). pp. 5863–5872.
  54. Hariharan, S., Jerusha, Y.A., Suganeshwari, G., Ibrahim, S.S., Tupakula, U., Varadharajan, V.: A hybrid deep learning model for network intrusion detection system using Seq2Seq and ConvLSTM-subnets. IEEE Access (2025). https://doi.org/10.1109/ACCESS.2025.3541399
    Article Google Scholar
  55. Singh, G., Sood, K., Rajalakshmi, P., Nguyen, D.D.N., Xiang, Y.: Evaluating federated learning-based intrusion detection scheme for next-generation networks. IEEE Trans. Net. Serv. Manag. (2024). https://doi.org/10.1109/TNSM.2024.3385385
    Article Google Scholar
  56. D. Kalaivani, 2025 An intrusion detection system using the NSL-KDD dataset's convolutional neural network and data analytics," in Leveraging Artificial Intelligence (AI) Competencies for Next-Generation Cybersecurity Solutions, Apple Academic Press. pp. 487–513.
  57. Malik, M., Ghous, H., Mubeen, M., Munir, A.M., Ahmad, N.: Intelligent intrusion detection system for internet of things using machine learning techniques. Int. J. Inf. Syst. Comput. Technol. 3(1), 23–39 (2024)
    Google Scholar
  58. Zhang, C.Q., Deng, Y., Chong, M.Z., Zhang, Z.W., Tan, Y.H.: Entropy-based re-sampling method on SAR class imbalance target detection. ISPRS J. Photogramm. Remote Sens. 209, 432–447 (2024)
    Article Google Scholar
  59. Eljialy, A.E.M., Uddin, M.Y., Ahmad, S.: Novel framework for an intrusion detection system using multiple feature selection methods based on deep learning. Tsinghua Sci. Technol. 29(4), 948–958 (2024)
    Article Google Scholar
  60. Zouhri, H., Idri, A., Ratnani, A.: Evaluating the impact of filter-based feature selection in intrusion detection systems. Int. J. Inf. Secur. 23(2), 759–785 (2024)
    Article Google Scholar
  61. Srivastav, S., Shukla, A.K., Kumar, S., Muhuri, P.K.: HYRIDE: HYbrid and Robust Intrusion DEtection approach for enhancing cybersecurity in Industry 4.0. Int. Things 30, 101492 (2025)
    Article Google Scholar
  62. Shirley, J.J., Priya, M.: An adaptive intrusion detection system for evolving IoT threats: an autoencoder-FNN fusion. IEEE Access 13, 1–17 (2025)
    Article Google Scholar
  63. Al-Haija, Q.A., Droos, A.: A comprehensive survey on deep learning-based intrusion detection systems in Internet of Things (IoT). Expert. Syst. 42(2), e13726 (2025)
    Article Google Scholar
  64. Nguyen, Q.H., Hore, S., Shah, A., Le, T., Bastian, N.D.: FedNIDS: a federated learning framework for packet-based network intrusion detection system. Digital Threats: Res. Pract. 6(1), 1–23 (2025)
    Article Google Scholar
  65. Wen, M., Zhang, Y., Zhang, P., Chen, L.: IDS-DWKAFL: an intrusion detection scheme based on dynamic weighted K-asynchronous federated learning for smart grid. J. Inform. Secur. Appl. 89, 103993 (2025)
    Google Scholar
  66. Thaljaoui, A.: Intelligent network intrusion detection system using optimized deep CNN-LSTM with UNSW-NB15. Int. J. Inform. Technol. (2025). https://doi.org/10.1007/s41870-025-02416-0
    Article Google Scholar
  67. Chen, Y., Yang, Y., Liang, Y., Zhu, T., Huang, D.: Federated learning with privacy preservation in large-scale distributed systems using differential privacy and homomorphic encryption. Informatica (2025). https://doi.org/10.31449/inf.v49i13.7358
    Article Google Scholar
  68. Bamber, S.S., Katkuri, A.V.R., Sharma, S., Angurala, M.: A hybrid CNN-LSTM approach for intelligent cyber intrusion detection system. Comput. Secur. 148, 104146 (2025)
    Article Google Scholar
  69. Thomas, S.G., Myakala, P.K.: Beyond the cloud: federated learning and edge AI for the next decade. J. Comput. Commun. 13(2), 37–50 (2025)
    Article Google Scholar
  70. Fang, H., Xu, L., Nan, G., Zheng, D., Zhao, H., Wang, X.: Accountable distributed access control with privacy preservation for blockchain-enabled internet of things systems: a zero-trust security scheme. IEEE Int. Things J. (2025). https://doi.org/10.1109/JIOT.2025.3540868
    Article Google Scholar
  71. Alshdadi, A.A., Almazroi, A.A., Ayub, N., Lytras, M.D., Alsolami, E., Alsubaei, F.S., Alharbey, R.: Federated deep learning for scalable and privacy-preserving distributed denial-of-service attack detection in internet of things networks. Fut. Int. 17(2), 88 (2025)
    Google Scholar
  72. Asperti, A., Raciti, G., Ronchieri, E., Cesini, D.: Machine learning-based anomaly prediction for proactive monitoring in data centers: a case study on INFN-CNAF. Appl. Sci. 15(2), 655 (2025)
    Article Google Scholar
  73. F. Pelekoudas-Oikonomou, P. H. Mirzaee, W. Hathal, G. Mantas, J. Rodriguez, H. Cruickshank, and Z. Sun, 2025 Federated learning-based intrusion detection systems for massive IoT,” in Security and Privacy for 6G Massive IoT, pp. 101–128
  74. Lu, S., Li, R., Liu, W.: FedDAA: a robust federated learning framework to protect privacy and defend against adversarial attack. Front. Comput. Sci. 18(2), 182307 (2024)
    Article Google Scholar
  75. Bai, J., Cao, L., Li, J., Wan, J., Du, X.: FedWDP: a Wasserstein-distance-based federated learning for privacy and heterogeneous data in IoT. Int. Things 31, 101532 (2025)
    Article Google Scholar
  76. Jiang, S., Wang, X., Que, Y., Lin, H.: Fed-MPS: Federated learning with local differential privacy using model parameter selection for resource-constrained CPS. J. Syst. Archit. 150, 103108 (2024)
    Article Google Scholar
  77. Mehedi, S.T., Abdulrazak, L.F., Ahmed, K., Uddin, M.S., Bui, F.M., Chen, L., Al-Zahrani, F.A.: A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences. Sci. Rep. 15(1), 7291 (2025)
    Article Google Scholar
  78. Zhang, R., Luo, W., Luo, Y., Zhang, H., Wang, J.: AFL-DCS: an asynchronous federated learning framework with dynamic client scheduling. Eng. Appl. Artif. Intell. 133, 107927 (2024)
    Article Google Scholar
  79. Mathina, P.A., Valarmathi, K.: Advancing IoT security: a novel intrusion detection system for evolving threats in Industry 4.0 using optimized convolutional sparse Ficks law graph point trans-Net. Comput. Secur. 148, 104169 (2025)
    Article Google Scholar
  80. Alotaibi, M., Mengash, H.A., Alqahtani, H., Al-Sharafi, A.M., Yahya, A.E., Alotaibi, S.R., Yafoz, A.: Hybrid GWQBBA model for optimized classification of attacks in intrusion detection system. Alex. Eng. J. 116, 9–19 (2025)
    Article Google Scholar
  81. V. Kumar, K. Kumar, M. Singh, and N. Kumar, 2025 NIDS-DA: Detecting functionally preserved adversarial examples for network intrusion detection system using deep autoencoders. Expert Systems with Applications. 126513
  82. X. Wu, Z. Jin, X. Chen, J. Zhou, and K. Liu, 2025 Boosting incremental intrusion detection system with adversarial samples. Expert Systems with Applications. 126632
  83. Liu, Y., Jia, Z., Jiang, Z., Lin, X., Liu, J., Wu, Q., Susilo, W.: BFL-SA: Blockchain-based federated learning via enhanced secure aggregation. J. Syst. Architect. 152, 103163 (2024)
    Article Google Scholar
  84. J. Wang, Q. Li, L. Lyu, and F. Ma, 2024 pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning." in The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS)
  85. Shi, Y., Fan, P., Zhu, Z., Peng, C., Wang, F., Letaief, K.B.: SAM: an efficient approach with selective aggregation of models in federated learning. IEEE Int. Things J. (2024). https://doi.org/10.1109/JIOT.2024.3373822
    Article Google Scholar
  86. Ni, L., Gong, X., Li, J., Tang, Y., Luan, Z., Zhang, J.: rFedFW: secure and trustable aggregation scheme for byzantine-robust federated learning in internet of things. Inf. Sci. 653, 119784 (2024)
    Article Google Scholar
  87. Yu, B., Zhao, J., Zhang, K., Gong, J., Qian, H.: Lightweight and dynamic privacy-preserving federated learning via functional encryption. IEEE Trans Inform. Foren. Secur. (2025). https://doi.org/10.1109/TIFS.2025.3540312
    Article Google Scholar
  88. Qu, Z., Zhao, X., Sun, L., Muhammad, G.: DAQFL: dynamic aggregation quantum federated learning algorithm for intelligent diagnosis in internet of medical things. IEEE Int. Things J. (2025). https://doi.org/10.1109/JIOT.2025.3537614
    Article Google Scholar
  89. Kumbhar, H.R., Rao, S.S.: Federated learning enabled multi-key homomorphic encryption. Expert Syst. Appl. 268, 126197 (2025)
    Article Google Scholar
  90. Chen, H., Mou, X., Wang, Z., Wu, T., Wang, X., Wang, C., Li, Y.: Multi-functional homomorphic encryption method based on crowd sensing networks. IEEE Access (2025). https://doi.org/10.1109/ACCESS.2025.3544763
    Article Google Scholar
  91. Zhou, T., Zhou, J., Cao, Z., Dong, X., Choo, K.K.R.: Efficient multilevel threshold changeable homomorphic data encapsulation with application to privacy-preserving vehicle positioning. IEEE Trans. Intell. Trans. Syst. (2025). https://doi.org/10.1109/TITS.2025.3525524
    Article Google Scholar
  92. Bondok, A.H., Badr, M.M., Mahmoud, M., El-Toukhy, A.T., Alsabaan, M., Amsaad, F., Ibrahem, M.I.: A Trojan Attack against smart grid federated learning and countermeasures. IEEE Access 12, 1–19 (2024)
    Article Google Scholar
  93. Hu, B., Guo, K., Wu, Z., Wen, X., Zhou, X.: Backdoor defense in transportation cyber-physical systems using frequency domain hybrid distillation. IEEE Trans. Intell. Transp. Syst. (2025). https://doi.org/10.1109/TITS.2025.3539887
    Article Google Scholar
  94. Paracha, A., Arshad, J., Farah, M.B., Ismail, K.: Outlier-oriented poisoning attack: a grey-box approach to disturb decision boundaries by perturbing outliers in multiclass learning. Int. J. Inf. Secur. 24(2), 85 (2025)
    Article Google Scholar
  95. M. A. Ferrag, M. Ndhlovu, N. Tihanyi, L. C. Cordeiro, M. Debbah, T. Lestable, and N. S. Thandi,
  96. Wang, L., Pan, C., Zhao, H., Ji, M., Wang, X., Yuan, J., Jiao, D.: Highly accurate adaptive federated forests based on resistance to adversarial attacks in wireless traffic prediction. Sensors 25(5), 1590 (2025)
    Article Google Scholar
  97. Chen, X., Tian, Y., Wang, S., Yang, K., Zhao, W., Xiong, J.: DBFL: dynamic Byzantine-robust privacy-preserving federated learning in heterogeneous data scenario. Inf. Sci. 700, 121849 (2025)
    Article Google Scholar
  98. Darzi, E., Dubost, F., Sijtsema, N.M., van Ooijen, P.M.: Exploring adversarial attacks in federated learning for medical imaging. IEEE Trans Indus Inform (2024). https://doi.org/10.1109/TII.2024.3423457
    Article Google Scholar
  99. Liu, D., Li, Z., Xu, D.: Generate universal adversarial perturbations by shortest-distance soft maximum direction attack. Comput. Secur. 150, 104168 (2025)
    Article Google Scholar
  100. Hassan, H.M.U., Rehmani, M.H., Chen, J.: Differential privacy techniques for cyber physical systems: asurvey. IEEE Commun. Surv. Tut. 22(1), 746–789 (2019)
    Article Google Scholar
  101. Fung, B.C., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: a survey of recent developments. ACM Computing Surveys (CSUR) 42(4), 1–53 (2010)
    Article Google Scholar
  102. Kim, J.W., Edemacu, K., Kim, J.S., Chung, Y.D., Jang, B.: A survey of differential privacy-based techniques and their applicability to location-based services. Comput. Secur. 111, 102464 (2021)
    Article Google Scholar
  103. Qu, Z., Zhang, L., Tiwari, P.: Quantum fuzzy federated learning for privacy protection in intelligent information processing. IEEE Trans. Fuzzy Syst. (2024). https://doi.org/10.1109/TFUZZ.2024.3419559
    Article Google Scholar
  104. Fan, L., Zhang, S., Kong, Y., Yi, X., Wang, Y., Xu, X.O., Shi, Y.: Evaluating the privacy valuation of personal data on smartphones. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 8(3), 1–33 (2024)
    Article Google Scholar
  105. Chen, H., Pang, J., Zhao, Y., Giddens, S., Ficek, J., Valente, M.J., Daley, E.: A data-driven approach to choosing privacy parameters for clinical trial data sharing under differential privacy. J. Am. Med. Inform. Assoc. 31, 1135–1143 (2024)
    Article Google Scholar
  106. Xie, H., Zhang, Y., Zhongwen, Z., Zhou, H.: Privacy-preserving medical data collaborative modeling: a differential privacy enhanced federated learning framework. J. Knowl. Learn. Sci. Technol. 3(4), 340–350 (2024)
    Google Scholar

Download references