Open Access
| Issue |
EPJ Web Conf.
Volume 367, 2026
Fifth International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2026)
|
|
|---|---|---|
| Article Number | 04008 | |
| Number of page(s) | 14 | |
| Section | AI & Machine Learning | |
| DOI | https://doi.org/10.1051/epjconf/202636704008 | |
| Published online | 29 April 2026 | |
- Hasan, Md Tarek, and Sai Praveen Kudapa. “Data privacy-aware machine learning and federated learning: A framework for data security.” American Journal of Interdisciplinary Studies 2, no. 03 (2021): 01–34. https://doi.org/10.63125/vj1hem03 [Google Scholar]
- Wan, Yichen, Youyang Qu, Wei Ni, Yong Xiang, Longxiang Gao, and Ekram Hossain. “Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey.” IEEE Communications Surveys & Tutorials 26, no. 3 (2024): 1861–1897. 10.1109/COMST.2024.3361451 [Google Scholar]
- Ali, Aitizaz, Hashim Ali, Aamir Saeed, Aftab Ahmed Khan, Ting Tin Tin, Muhammad Assam, Yazeed Yasin Ghadi, and Heba G. Mohamed. “Blockchain-powered healthcare systems: enhancing scalability and security with hybrid deep learning.” Sensors 23, no. 18 (2023): 7740. https://doi.org/10.3390/s23187740 [Google Scholar]
- Zheng, Yifeng, Shangqi Lai, Yi Liu, Xingliang Yuan, Xun Yi, and Cong Wang. “Aggregation service for federated learning: An efficient, secure, and more resilient realization.” IEEE Transactions on Dependable and Secure Computing 20, no. 2 (2022): 988–1001. 10.1109/TDSC.2022.3146448 [Google Scholar]
- Albshaier, Latifa, Seetah Almarri, and Abdullah Albuali. “Federated learning for cloud and edge security: A systematic review of challenges and AI opportunities.” Electronics 14, no. 5 (2025): 1019. https://doi.org/10.3390/electronics14051019 [Google Scholar]
- Huang, Jia, Zhen Chen, Sheng-Zheng Liu, Hao Zhang, and Hai-Xia Long. “Improved intrusion detection based on hybrid deep learning models and federated learning.” Sensors 24, no. 12 (2024): 4002. https://doi.org/10.3390/s24124002 [Google Scholar]
- Lavaur, Léo, Marc-Oliver Pahl, Yann Busnel, and Fabien Autrel. “The evolution of federated learning-based intrusion detection and mitigation: a survey.” IEEE Transactions on Network and Service Management 19, no. 3 (2022): 2309–2332. 10.1109/TNSM.2022.3177512 [Google Scholar]
- Kamat, Pooja, Rekha Sugandhi, and Satish Kumar. “Data-driven bearing fault detection using a hybrid autoencoder-LSTM deep learning approach.” International Journal of Modelling, Identification and Control 38, no. 1 (2021): 88–103. https://doi.org/10.1504/IJMIC.2021.122471 [Google Scholar]
- Batur Şahin, Canan. “Securing UAV Swarms with Vision Transformers: A Byzantine- Robust Federated Learning Framework for Cross-Modal Intrusion Detection.” Drones 10, no. 2 (2026): 125. https://doi.org/10.3390/drones10020125 [Google Scholar]
- Puviarasu, A., and V. K. Sudha. “Enhanced IoT security: privacy-preserving federated learning model for accurate, real-time intrusion detection across devices.” Ain Shams Engineering Journal 17, no. 1 (2026): 103866. https://doi.org/10.1016/j.asej.2025.103866 [Google Scholar]
- Ramalingam, Venkadeshan, Basant Kumar, Shashi Kant Gupta, Deema Mohammed Alsekait, and Diaa Salama AbdElminaam. “A hybrid federated learning framework with generative AI for privacy-preserving and sustainable security in IOT-enabled smart environments.” Scientific Reports 16, no. 1 (2026): 3071. https://doi.org/10.1038/s41598-025-31769-6 [Google Scholar]
- Mankotia, Sameer, Daniel Conte de Leon, and Bhaskar P. Rimal. “FedPrIDS: Privacy- Preserving Federated Learning for Collaborative Network Intrusion Detection in IoT.” Journal of Cybersecurity and Privacy 6, no. 1 (2026): 10. https://doi.org/10.3390/jcp6010010 [Google Scholar]
- Ali, Anas, Mubashar Husain, and Peter Hans. “Federated learning-enhanced blockchain framework for privacy-preserving intrusion detection in industrial iot.” arXiv preprint arXiv:2505.15376 (2025). https://doi.org/10.48550/arXiv.2505.15376 [Google Scholar]
- Shalan, M., M. R. Hasan, Y. Bai, and J. Li. Enhancing Smart Home Security: Blockchain-Enabled Federated Learning with Knowledge Distillation for Intrusion Detection. Smart Cities 2025, 8, 35. https://doi.org/10.3390/smartcities8010035 [Google Scholar]
- Chandu, Gutti, Thumula Karthik, and Balbudhe Parag. “Federated learning for distributed IoT security: A privacy-preserving approach to intrusion detection.” IEEE Access (2025). 10.1109/ACCESS.2025.3592481 [Google Scholar]
- Timofte, Edi Marian, Mihai Dimian, Adrian Graur, Alin Dan Potorac, Doru Balan, Ionut Croitoru, Daniel-Florin Hrițcan, and Marcel Pușcașu. “Federated learning for cybersecurity: A privacy-preserving approach.” Applied Sciences 15, no. 12 (2025): 6878. https://doi.org/10.3390/app15126878 [Google Scholar]
- Javeed, Danish, Muhammad Shahid Saeed, Muhammad Adil, Prabhat Kumar, and Alireza Jolfaei. “A federated learning-based zero trust intrusion detection system for Internet of Things.” Ad Hoc Networks 162 (2024): 103540. https://doi.org/10.1016/j.adhoc.2024.103540 [Google Scholar]
- Alazab, Ammar, Ansam Khraisat, Sarabjot Singh, and Tony Jan. “Enhancing privacy- preserving intrusion detection through federated learning.” Electronics 12, no. 16 (2023): 3382. https://doi.org/10.3390/electronics12163382 [Google Scholar]
- Rashid, Md Mamunur, Shahriar Usman Khan, Fariha Eusufzai, Md Azharuddin Redwan, Saifur Rahman Sabuj, and Mahmoud Elsharief. “A federated learning-based approach for improving intrusion detection in industrial internet of things networks.” Network 3, no. 1 (2023): 158–179. https://doi.org/10.3390/network3010008 [Google Scholar]
- Awan, Kamran Ahmad, Ikram Ud Din, Mahdi Zareei, Ahmad Almogren, Byung Seo-Kim, and Jesús Arturo Pérez-Díaz. “Securing iot with deep federated learning: A trust- based malicious node identification approach.” IEEE Access 11 (2023): 58901–58914. 10.1109/ACCESS.2023.3284677 [Google Scholar]
- Ava, Smith. “Privacy-Preserving Federated Learning for Zero-Trust Security Enforcement.” Available at SSRN 5891085 (2022). Ava, Smith, Privacy-Preserving Federated Learning for Zero-Trust Security Enforcement (May 07, 2022). Available at SSRN: https://ssrn.com/abstract=5891085 or http://dx.doi.org/10.2139/ssrn.5891085 [Google Scholar]
- Attota, Dinesh Chowdary, Viraaji Mothukuri, Reza M. Parizi, and Seyedamin Pouriyeh. “An ensemble multi-view federated learning intrusion detection for IoT.” IEEE Access 9 (2021): 117734–117745. 10.1109/ACCESS.2021.3107337 [Google Scholar]
- Babbar, Himanshi, Shalli Rani, and Mohammad Shabaz. “Federated learning with enhanced cryptographic security for vehicular cyber-physical systems.” Scientific Reports 15, no. 1 (2025): 28593. https://doi.org/10.1038/s41598-025-14341-0 [Google Scholar]
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