Smart City Gnosys

Smart city article details

Title	Rpifl: Reliable And Privacy-Preserving Federated Learning For The Internet Of Things
ID_Doc	47112
Authors	Wang R.; Lai J.; Li X.; He D.; Khan M.K.
Year	2024
Published	Journal of Network and Computer Applications, 221
DOI	http://dx.doi.org/10.1016/j.jnca.2023.103768
Abstract	Federated Learning for the Internet of Things(FL for IoT) contributes to the enhancement of security in smart cities. However, the privacy disclosure attacks within the FL for IoT framework exposes user's local data. Although differential privacy mechanism (DP) ensures the privacy of IoT data, one crucial issue is DP affects model accuracy and reduces model reliability. Besides, DP cannot provide comprehensive privacy preservation in the IoT environment. To tackle the above problems, we propose reliable federated learning with privacy-preserving for IoT(RPIFL). Our approach comprises three key components. First, we leverage a lightweight network to hide the private data locally, then use the output to perform the next model training. Second, we propose a multi-modal fine-grained model optimization, which significantly improves the accuracy of the IoT model. Third, we implement adaptive differential privacy mechanisms tailored to dynamic privacy requirements, thereby safeguarding the confidentiality of IoT data. To empirically validate our framework, we conducted a simulation experiment on an image classification dataset. Our methodology concurrently ensures privacy protection and achieves superior accuracy compared to the baseline model with an average accuracy increase of 1.1% over the two prevailing federated privacy protection methods, coupled with an average reduction in convergence time of over 9%. © 2023 Elsevier Ltd
Author Keywords	Differential privacy; Federated learning; Internet of Things; Multimodal fine-grained

Similar Articles

Id	Similarity	Authors	Title	Published
43157	0.912	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)
23846	0.906	Karimy A.U.; Reddy P.C.	Enhancing Iot Security: A Novel Approach With Federated Learning And Differential Privacy Integration	International Journal of Computer Networks and Communications, 16, 4 (2024)
27631	0.898	Zhang Z.; Wu L.; He D.; Wang Q.; Wu D.; Shi X.; Ma C.	G-Vcfl: Grouped Verifiable Chained Privacy-Preserving Federated Learning	IEEE Transactions on Network and Service Management, 19, 4 (2022)
46858	0.897	Zhou H.; Dai H.; Yang G.; Xiang Y.	Robust Federated Learning For Privacy Preservation And Efficiency In Edge Computing	IEEE Transactions on Services Computing, 18, 3 (2025)
26365	0.89	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)
28663	0.889	Sumitra; Sharma J.; Shenoy M.V.	Hafedl: A Hessian-Aware Adaptive Privacy Preserving Horizontal Federated Learning Scheme For Iot Applications	IEEE Access, 12 (2024)
43182	0.888	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	0.888	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)
54221	0.886	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)
47639	0.886	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)