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Towards an offloading approach that augments multi-persona performance and viability.

, , , and . CCNC, page 455-460. IEEE, (2015)

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Nouveaux points de coupure et primitives pour les préoccupations de renforcement de sécurité., , , and . Technique et Science Informatiques, 28 (5): 611-644 (2009)Security hardening of open source software., , and . PST, volume 380 of ACM International Conference Proceeding Series, page 43. ACM, (2006)A cluster-based model for QoS-OLSR protocol., , , , and . IWCMC, page 1099-1104. IEEE, (2011)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)Sampling Online Social Networks with Tailored Mining Strategies., , , and . SNAMS, page 217-222. IEEE, (2019)Reinforcement R-learning model for time scheduling of on-demand fog placement., , and . J. Supercomput., 76 (1): 388-410 (2020)Reward shaping using convolutional neural network., , , , and . Inf. Sci., (November 2023)Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems., , , and . IEEE Commun. Surv. Tutorials, 23 (2): 1342-1397 (2021)Reinforcement Learning Based Scheme for On-Demand Vehicular Fog Formation and Micro Services Placement., , and . IWCMC, page 1244-1249. IEEE, (2022)Overcoming Resource Bottlenecks in Vehicular Federated Learning: A Cluster-Based and QoS-Aware Approach., , , and . GLOBECOM, page 419-424. IEEE, (2023)