Smart City Gnosys
Smart city article details
| Title | Privacy-Preserved Learning From Non-I.I.D Data In Fog-Assisted Iot: A Federated Learning Approach |
|---|---|
| ID_Doc | 43157 |
| Authors | Abdel-Basset M.; Hawash H.; Moustafa N.; Razzak I.; Abd Elfattah M. |
| Year | 2024 |
| Published | Digital Communications and Networks, 10, 2 |
| DOI | http://dx.doi.org/10.1016/j.dcan.2022.12.013 |
| Abstract | With the prevalence of the Internet of Things (IoT) systems, smart cities comprise complex networks, including sensors, actuators, appliances, and cyber services. The complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks, especially privacy-related attacks such as inference and data poisoning ones. Federated Learning (FL) has been regarded as a hopeful method to enable distributed learning with privacy-preserved intelligence in IoT applications. Even though the significance of developing privacy-preserving FL has drawn as a great research interest, the current research only concentrates on FL with independent identically distributed (i.i.d) data and few studies have addressed the non-i. i.d setting. FL is known to be vulnerable to Generative Adversarial Network (GAN) attacks, where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other contributors. This paper proposes an innovative Privacy Protection-based Federated Deep Learning (PP-FDL) framework, which accomplishes data protection against privacy-related GAN attacks, along with high classification rates from non-i. i.d data. PP-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other, where class probabilities are protected utilizing a private identifier generated for each class. The PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 datasets. The empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8% as accuracy improvements. © 2022 Chongqing University of Posts and Telecommunications |
| Author Keywords | Deep learning; Federated learning; Fog computing; Privacy preservation; Smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
47112  | 0.912 | Wang R.; Lai J.; Li X.; He D.; Khan M.K. | Rpifl: Reliable And Privacy-Preserving Federated Learning For The Internet Of Things | Journal of Network and Computer Applications, 221 (2024) |
| 43182 | 0.887 | Zhu J.; Wu J.; Bashir A.K.; Pan Q.; Yang W. | Privacy-Preserving Federated Learning Of Remote Sensing Image Classification With Dishonest Majority | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16 (2023) |
| 28562 | 0.887 | 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) |
| 26365 | 0.887 | Aggarwal M.; Khullar V.; Rani S.; André Prola T.; Bhattacharjee S.B.; Shawon S.M.; Goyal N. | Federated Learning On Internet Of Things: Extensive And Systematic Review | Computers, Materials and Continua, 79, 2 (2024) |
| 7427 | 0.887 | 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) |
| 26315 | 0.885 | Utomo S.; Rouniyar A.; Hsu H.-C.; Hsiung P.-A. | Federated Adversarial Training Strategies For Achieving Privacy And Security In Sustainable Smart City Applications | Future Internet, 15, 11 (2023) |
| 43179 | 0.884 | Tan Z.-S.; See-To E.W.K.; Lee K.-Y.; Dai H.-N.; Wong M.-L. | Privacy-Preserving Federated Learning For Proactive Maintenance Of Iot-Empowered Multi-Location Smart City Facilities | Journal of Network and Computer Applications, 231 (2024) |
| 47639 | 0.883 | Hijazi N.M.; Aloqaily M.; Guizani M.; Ouni B.; Karray F. | Secure Federated Learning With Fully Homomorphic Encryption For Iot Communications | IEEE Internet of Things Journal, 11, 3 (2024) |
| 26392 | 0.882 | Tabassum A.; Erbad A.; Lebda W.; Mohamed A.; Guizani M. | Fedgan-Ids: Privacy-Preserving Ids Using Gan And Federated Learning | Computer Communications, 192 (2022) |
| 54221 | 0.879 | Yang H.; Liu H.; Yuan X.; Wu K.; Ni W.; Zhang J.A.; Liu R.P. | Synergizing Intelligence And Privacy: A Review Of Integrating Internet Of Things, Large Language Models, And Federated Learning In Advanced Networked Systems | Applied Sciences (Switzerland), 15, 12 (2025) |