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FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices., , , , , , and . CoRR, (2022)Coalitional Federated Learning: Improving Communication and Training on Non-IID Data With Selfish Clients., , , and . IEEE Trans. Serv. Comput., 16 (4): 2462-2476 (July 2023)Towards Bilateral Client Selection in Federated Learning Using Matching Game Theory., , , , , and . GLOBECOM, page 1-6. IEEE, (2022)Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities., , , , , , , and . CoRR, (2024)FoGMatch: An Intelligent Multi-Criteria IoT-Fog Scheduling Approach Using Game Theory., , , , and . IEEE/ACM Trans. Netw., 28 (4): 1779-1789 (2020)FedMint: Intelligent Bilateral Client Selection in Federated Learning With Newcomer IoT Devices., , , , , , and . IEEE Internet Things J., 10 (23): 20884-20898 (December 2023)Machine Learning Based Container Placement in On-Demand Clustered Fogs., , , , and . IWCMC, page 1250-1255. IEEE, (2022)A Survey on IoT Intrusion Detection: Federated Learning, Game Theory, Social Psychology, and Explainable AI as Future Directions., , , , and . IEEE Internet Things J., 10 (5): 4059-4092 (March 2023)The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions., , , , , , , , , and 7 other author(s). CoRR, (2023)Towards Instant Clustering Approach for Federated Learning Client Selection., , , and . ICNC, page 409-413. IEEE, (2023)