Reverse polarity optical Orthogonal frequency Division Multiplexing for High-Speed visible light communications system

Muttashar Abdulsahib, Ghaida; Sekaran Selvaraj, Dhana; Manikandan, A.; Palanisamy, SatheeshKumar; Uddin, Mueen; Ibrahim Khalaf, Osamah; Abdelhaq, Maha; Alsaqour, Raed

doi:10.1016/j.eij.2023.100407

Cited by 13 publications

(2 citation statements)

References 30 publications

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“…Furthermore, to benchmark our method against the latest State‐Of‐The‐Art (SOTA) in FL privacy protection, we have incorporated referencing studies 26 and 27 as controls within our experimentation. Additionally, references [28–30] aim to ensure a rigorous comparison against established privacy protection mechanisms and highlight the advancements achieved by our proposed approach. We believe these enhancements significantly contribute to the robustness and relevance of our experimental section.…”

Section: Resultsmentioning

confidence: 99%

Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing

Ibrahim Khalaf,

S.R,

Algburi

et al. 2024

Security and Privacy

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The federated learning has gained prominent attention as a collaborative machine learning method, allowing multiple users to jointly train a shared model without directly exchanging raw data. This research addresses the fundamental challenge of balancing data privacy and utility in distributed learning by introducing an innovative hybrid methodology fusing differential privacy with federated learning (HDP‐FL). Through meticulous experimentation on EMNIST and CIFAR‐10 data sets, this hybrid approach yields substantial advancements, showcasing a noteworthy 4.22% and up to 9.39% enhancement in model accuracy for EMNIST and CIFAR‐10, respectively, compared to conventional federated learning methods. Our adjustments to parameters highlighted how noise impacts privacy, showcasing the effectiveness of our hybrid DP approach in striking a balance between privacy and accuracy. Assessments across diverse FL techniques and client counts emphasized this trade‐off, particularly in non‐IID data settings, where our hybrid method effectively countered accuracy declines. Comparative analyses against standard machine learning and state‐of‐the‐art FL approaches consistently showcased the superiority of our proposed model, achieving impressive accuracies of 96.29% for EMNIST and 82.88% for CIFAR‐10. These insights offer a strategic approach to securely collaborate and share knowledge among IoT devices without compromising data privacy, ensuring efficient and reliable learning mechanisms across decentralized networks.

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Section: Resultsmentioning

confidence: 99%

Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing

Ibrahim Khalaf,

S.R,

Algburi

et al. 2024

Security and Privacy

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“…This analysis is independent of the feeding technique, and the materials should be lossless. Using surface integration equations and the Method of Moments (MoM) [ 32 ], characteristic modes are numerically calculated. Antenna design can be systematically implemented without trial and error method.…”

Section: Characteristic Mode Analysismentioning

confidence: 99%