Smart City Gnosys
Smart city article details
| Title | Federated Adversarial Training Strategies For Achieving Privacy And Security In Sustainable Smart City Applications |
|---|---|
| ID_Doc | 26315 |
| Authors | Utomo S.; Rouniyar A.; Hsu H.-C.; Hsiung P.-A. |
| Year | 2023 |
| Published | Future Internet, 15, 11 |
| DOI | http://dx.doi.org/10.3390/fi15110371 |
| Abstract | Smart city applications that request sensitive user information necessitate a comprehensive data privacy solution. Federated learning (FL), also known as privacy by design, is a new paradigm in machine learning (ML). However, FL models are susceptible to adversarial attacks, similar to other AI models. In this paper, we propose federated adversarial training (FAT) strategies to generate robust global models that are resistant to adversarial attacks. We apply two adversarial attack methods, projected gradient descent (PGD) and the fast gradient sign method (FGSM), to our air pollution dataset to generate adversarial samples. We then evaluate the effectiveness of our FAT strategies in defending against these attacks. Our experiments show that FGSM-based adversarial attacks have a negligible impact on the accuracy of global models, while PGD-based attacks are more effective. However, we also show that our FAT strategies can make global models robust enough to withstand even PGD-based attacks. For example, the accuracy of our FAT-PGD and FL-mixed-PGD models is 81.13% and 82.60%, respectively, compared to 91.34% for the baseline FL model. This represents a reduction in accuracy of 10%, but this could be potentially mitigated by using a more complex and larger model. Our results demonstrate that FAT can enhance the security and privacy of sustainable smart city applications. We also show that it is possible to train robust global models from modest datasets per client, which challenges the conventional wisdom that adversarial training requires massive datasets. |
| Author Keywords | adversarial attack; federated learning; privacy protection; robust model; sustainable smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
43157  | 0.885 | Abdel-Basset M.; Hawash H.; Moustafa N.; Razzak I.; Abd Elfattah M. | Privacy-Preserved Learning From Non-I.I.D Data In Fog-Assisted Iot: A Federated Learning Approach | Digital Communications and Networks, 10, 2 (2024) |
| 7427 | 0.885 | Wahida F.; Chamikara M.A.P.; Khalil I.; Atiquzzaman M. | An Adversarial Machine Learning Based Approach For Privacy Preserving Face Recognition In Distributed Smart City Surveillance | Computer Networks, 254 (2024) |
| 26392 | 0.873 | Tabassum A.; Erbad A.; Lebda W.; Mohamed A.; Guizani M. | Fedgan-Ids: Privacy-Preserving Ids Using Gan And Federated Learning | Computer Communications, 192 (2022) |
| 26353 | 0.872 | Kapoor A.; Kumar D. | Federated Learning For Urban Sensing Systems: A Comprehensive Survey On Attacks, Defences, Incentive Mechanisms, And Applications | IEEE Communications Surveys and Tutorials, 27, 2 (2025) |
| 26340 | 0.872 | Hamid S.; Bawany N.Z. | Federated Learning For Enhanced Intrusion Detection In Smart City Environments | 2024 18th International Conference on Open Source Systems and Technologies, ICOSST 2024 - Proceedings (2024) |
| 26359 | 0.87 | Gandhi M.; Singh S.K.; Ravikumar R.N.; Vaghela K. | Federated Learning In Secure Smart City Sensing: Challenges And Opportunities | Edge of Intelligence: Exploring the Frontiers of AI at the Edge (2025) |
| 28562 | 0.869 | Vasa J.; Thakkar A.; Bhavsar D.; Patel P. | Guarding Privacy In Federated Learning: Exploring Threat Landscapes And Countermeasures With Case Studies | Lecture Notes in Networks and Systems, 1194 (2025) |
| 22956 | 0.866 | Djenouri Y.; Belbachir A.N. | Empowering Urban Connectivity In Smart Cities Using Federated Intrusion Detection | 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings (2023) |
| 26347 | 0.862 | Alla K.R.; Thangarasu G. | Federated Learning For Iot Devices In Smart Cities: A Particle Swarm Optimation-Based Approach | 2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023 (2023) |
| 28274 | 0.861 | Xu H.; Seng K.P.; Ang L.-M.; Wang W.; Smith J. | Graph Split Federated Learning For Distributed Large-Scale Aiot In Smart Cities | IEEE Open Journal of the Computer Society, 6 (2025) |