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Towards On-Demand Deployment of Multiple Clients and Heterogeneous Models in Federated Learning.

, , , , , and . IWCMC, page 1556-1561. IEEE, (2023)

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Towards On-Demand Deployment of Multiple Clients and Heterogeneous Models in Federated Learning., , , , , and . IWCMC, page 1556-1561. IEEE, (2023)AI, Blockchain, and Vehicular Edge Computing for Smart and Secure IoV: Challenges and Directions., , , , , and . IEEE Internet Things Mag., 3 (2): 68-73 (2020)On The Use of Software Defined Wireless Network in Vehicular Fog Computing Environments., , , and . IWCMC, page 1198-1203. IEEE, (2019)CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication., , , , , , and . J. Netw. Comput. Appl., (2024)Reward shaping using convolutional neural network., , , , and . Inf. Sci., (November 2023)Reinforcement R-learning model for time scheduling of on-demand fog placement., , and . J. Supercomput., 76 (1): 388-410 (2020)Vehicular-OBUs-As-On-Demand-Fogs: Resource and Context Aware Deployment of Containerized Micro-Services., , and . IEEE/ACM Trans. Netw., 28 (2): 778-790 (2020)FScaler: Automatic Resource Scaling of Containers in Fog Clusters Using Reinforcement Learning., , , and . IWCMC, page 1824-1829. IEEE, (2020)On-Demand-FL: A Dynamic and Efficient Multicriteria Federated Learning Client Deployment Scheme., , , , , , and . IEEE Internet Things J., 10 (18): 15822-15834 (September 2023)