Smart City Gnosys

Smart city article details

Title	Towards Mutual Trust-Based Matching For Federated Learning Client Selection
ID_Doc	58229
Authors	Wehbi O.; Wahab O.A.; Mourad A.; Otrok H.; Alkhzaimi H.; Guizani M.
Year	2023
Published	2023 International Wireless Communications and Mobile Computing, IWCMC 2023
DOI	http://dx.doi.org/10.1109/IWCMC58020.2023.10182581
Abstract	Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart cities are areas that can generate high volume and critical data, which has the potential to revolutionize federated learning. Nevertheless, it is highly challenging to select a trustworthy group of clients to collaborate in model training. The utilization of a random selection technique would pose many threats due to malicious clients' targeted and untargeted attacks. Such vulnerability may cause attacks and poisoning in the produced model. To address this problem, we present a mutual trust client-server selection approach based on matching game theory and bootstrapping mechanisms for federated learning in smart cities. Our solution entails the creation of: (1) preference functions for federated servers and smart devices (i.e., IoT/IoV) that enables them to sort each other based on trust score, (2) light feedback-base technique that leverages the cooperation of multiple client devices to assign trust value to the newly connected federated server, and (3) intelligent matching algorithms consider trust preferences of both parties in their design. According to our simulation results, our technique outperforms the baseline selection approach VanillaFL in terms of increasing the trust level and hence the global accuracy of the federated learning model and optimizing the number of untrusted selected clients. © 2023 IEEE.
Author Keywords	and Bootstrapping; Federated Learning; Game Theory; Mutual trust; Smart devices; Smart-cities

Similar Articles

Id	Similarity	Authors	Title	Published
23871	0.945	Wehbi O.; Arisdakessian S.; Guizani M.; Wahab O.A.; Mourad A.; Otrok H.; Khzaimi H.A.; Ouni B.	Enhancing Mutual Trustworthiness In Federated Learning For Data-Rich Smart Cities	IEEE Internet of Things Journal, 12, 3 (2025)
7806	0.878	Yin X.; Qiu H.; Wu X.; Zhang X.	An Efficient Attribute-Based Participant Selecting Scheme With Blockchain For Federated Learning In Smart Cities	Computers, 13, 5 (2024)
45392	0.875	Huang S.; Liang Y.; Shen F.; Gao F.	Research On Federated Learning'S Contribution To Trustworthy And Responsible Artificial Intelligence	ACM International Conference Proceeding Series (2024)
5652	0.873	Wang S.; Chen C.; Han B.; Zhu J.	A Trusted And Decentralized Federated Learning Framework For Iot Devices In Smart City	Proceedings - IEEE Congress on Cybermatics: 2024 IEEE International Conferences on Internet of Things, iThings 2024, IEEE Green Computing and Communications, GreenCom 2024, IEEE Cyber, Physical and Social Computing, CPSCom 2024, IEEE Smart Data, SmartData 2024 (2024)
37368	0.871	Albelaihi R.	Mobility Prediction And Resource-Aware Client Selection For Federated Learning In Iot	Future Internet, 17, 3 (2025)
25057	0.871	Bryan Lim W.Y.; Shyuan Ng J.; Nie J.; Hu Q.; Xiong Z.; Niyato D.; Miao C.	Evolutionary Model Owner Selection For Federated Learning With Heterogeneous Privacy Budgets	IEEE International Conference on Communications, 2022-May (2022)
47130	0.869	Wen T.; Zhang H.; Zhang H.; Wu H.; Wang D.; Liu X.; Zhang W.; Wang Y.; Cao S.	Rtifed: A Reputation Based Triple-Step Incentive Mechanism For Energy-Aware Federated Learning Over Battery-Constricted Devices	Computer Networks, 241 (2024)
28562	0.866	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)
924	0.862	Mehdi M.; Makkar A.; Conway M.	A Comprehensive Review Of Open-Source Federated Learning Frameworks	Procedia Computer Science, 260 (2025)
54221	0.86	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)