Towards Bilateral Client Selection in Federated Learning Using Matching Game Theory
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[1] C. Liu,et al. Participant Selection for Federated Learning With Heterogeneous Data in Intelligent Transport System , 2023, IEEE Transactions on Intelligent Transportation Systems.
[2] A. Mourad,et al. On Demand Fog Federations for Horizontal Federated Learning in IoV , 2022, IEEE Transactions on Network and Service Management.
[3] H. Otrok,et al. IoT Sensor Selection for Target Localization: A Reinforcement Learning based Approach , 2022, Ad Hoc Networks.
[4] Hadi Otrok,et al. Machine Learning in Mobile Crowd Sourcing: A Behavior-Based Recruitment Model , 2021, ACM Trans. Internet Techn..
[5] Ernesto Damiani,et al. How Artificial Intelligence and Mobile Crowd Sourcing are Inextricably Intertwined , 2021, IEEE Network.
[6] Aruna Seneviratne,et al. Federated Learning for Internet of Things: A Comprehensive Survey , 2021, IEEE Communications Surveys & Tutorials.
[7] Mohsen Guizani,et al. A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond , 2021, IEEE Internet of Things Journal.
[8] Azzam Mourad,et al. FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning , 2021, IEEE Internet of Things Journal.
[9] Tarik Taleb,et al. Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems , 2021, IEEE Communications Surveys & Tutorials.
[10] Zhu Han,et al. Matching-Theory-Based Low-Latency Scheme for Multitask Federated Learning in MEC Networks , 2021, IEEE Internet of Things Journal.
[11] Albert Y. Zomaya,et al. An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee , 2020, IEEE Transactions on Parallel and Distributed Systems.
[12] Jianyu Wang,et al. Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies , 2020, ArXiv.
[13] Lewis Tseng,et al. Blockchain and Fog Computing for Cyberphysical Systems: The Case of Smart Industry , 2020, Computer.
[14] Azzam Mourad,et al. FoGMatch: An Intelligent Multi-Criteria IoT-Fog Scheduling Approach Using Game Theory , 2020, IEEE/ACM Transactions on Networking.
[15] Jamal Bentahar,et al. AI, Blockchain, and Vehicular Edge Computing for Smart and Secure IoV: Challenges and Directions , 2020, IEEE Internet of Things Magazine.
[16] Richard Nock,et al. Advances and Open Problems in Federated Learning , 2019, Found. Trends Mach. Learn..
[17] Anuj Kumar,et al. Active Federated Learning , 2019, ArXiv.
[18] Ernesto Damiani,et al. Machine Learning-Based Framework for Log-Lifting in Business Process Mining Applications , 2019, BPM.
[19] Takayuki Nishio,et al. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge , 2018, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).
[20] Peter Richtárik,et al. Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.
[21] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[22] O. A. Wahab,et al. Intrusion Detection in the IoT Under Data and Concept Drifts: Online Deep Learning Approach , 2022, IEEE Internet of Things Journal.