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Chamseddine Talhi
Researcher at École de technologie supérieure
Publications - 93
Citations - 1666
Chamseddine Talhi is an academic researcher from École de technologie supérieure. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 17, co-authored 72 publications receiving 896 citations. Previous affiliations of Chamseddine Talhi include Concordia University & Laval University.
Papers
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Journal ArticleDOI
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond
Sawsan Abdulrahman,Hanine Tout,Hakima Ould-Slimane,Azzam Mourad,Chamseddine Talhi,Mohsen Guizani +5 more
TL;DR: In this article, a survey of federated learning (FL) topics and research fields is presented, including core system models and designs, application areas, privacy and security, and resource management.
Journal ArticleDOI
Internet of Things intrusion Detection: Centralized, On-Device, or Federated Learning?
TL;DR: Experimental results and empirical analysis explore the robustness and advantages of the proposed Federated Learning detection model by reaching an accuracy close to that of the centralized approach and outperforming the distributed unaggregated on-device trained models.
Journal ArticleDOI
FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning
TL;DR: FedMCCS is proposed, a multicriteria-based approach for client selection in federated learning that outperforms the other approaches by reducing the number of communication rounds to reach the intended accuracy and handling the least number of discarded rounds.
Patent
Multi-tenant isolation in a cloud environment using software defined networking
TL;DR: In this article, a switch is used to de-multiplex incoming traffic between a number of data centers tenants and to direct traffic to the appropriate virtual slice for an identified tenant.
Journal ArticleDOI
An anomaly detection system based on variable N-gram features and one-class SVM
TL;DR: A new anomaly detection system using OC-SVM with a Gaussian kernel, trained on a novel feature extraction technique, which achieves a higher-level of detection accuracy than that achieved by Markovian and n-gram based models as well as by the state-of-the-art anomaly detection techniques.