FLEAD: Federated Learning for IoT Anomaly Detection using Kafka, Flink, Spark<ul><li>Built a federated learning setup for anomaly detection across distributed IoT devices.</li><li>Implemented real-time streaming pipelines and batch evaluation workflows.</li><li>Analyzed model behavior under heterogeneous and non-IID data settings.&nbsp;</li></ul>

FLEAD: Federated Learning for IoT Anomaly Detection 

I've had the opportunity to develop a full-stack website for Skate Consult company , and we are pleased to announce the compilation of the first version of the platform. Check out the live preview: https://skate.dz🔹 Frontend: Built with React and Tailwind CSS for a modern, responsive, and user-friendly interface. 🔹 Backend: Developed using Node.js and Express, ensuring robust and scalable server-side operations. 🔹 Database: Utilized MongoDB for efficient and flexible data storage. 🔹 Authentication: Implemented JWT authentication to secure user data and interactions.🔹 Multi-Language Support: The platform supports both Arabic and English, enhancing accessibility for a diverse user base.🔹 Dashboard: Designed and developed a comprehensive dashboard enabling streamlined access to website content and efficient user management.The result is a secure, user-friendly platform that meets Skate Company's needs. I'm proud of the results we've achieved and grateful for the opportunity to work with Fouzia BENMADI.

Skate Consult Platform

Overview: This project is a fully functional e-learning platform built with React and Node.js, designed to support online education through a structured course and program management system. The platform handles the complete user journey — from browsing and enrolling in courses to submitting payments and receiving admin approval — while also supporting scholarship, internship, fellowship, and grant programs. It includes a rich text editor, FAQ system, review and rating system, and a real-time messaging interface between users and administrators. Technical &amp; Admin: On the technical side, the platform integrates Bunny Media CDN for video and PDF hosting, a coupon and discount system, and QR code generation utilities. The admin dashboard provides full control over user management, content moderation, payment processing, and dynamic landing page configuration. It also features a comprehensive analytics module tracking visits, revenue, user growth, and search behavior, enabling data-driven decisions. The system was built with scalability and professionalism in mind, with server-side error logging and a clean separation between user-facing and administrative interfaces.

Elearning / Scholarships managment system 

﻿Optimization-Based Multi-Objective Task Offloading in IoT–Fog–Cloud Systems: An Energy–Delay Trade-off Framework&nbsp;Research Context: This is an ongoing thesis research project focused on the problem of task offloading in IoT–Fog–Cloud computing architectures, where devices must decide whether to process tasks locally or offload them to fog or cloud nodes. The core challenge is simultaneously minimizing energy consumption and execution delay while satisfying hard constraints such as task deadlines, memory limits, and node capacity. The work begins by rigorously formulating this as a constrained multi-objective optimization problem with well-defined variables, objectives, feasibility rules, and benchmark scenario specifications.Paragraph 2 — Methodology: The proposed solution involves implementing a baseline NSGA-II evolutionary solver, then enhancing it with a hybrid operator — either a Differential Evolution-based crossover or a local refinement module — to improve Pareto-front quality, convergence stability, and solution diversity. Ablation studies will be conducted to isolate the contribution of each component, and a practical selection rule will be defined for choosing operating points on the Pareto front. The final phase involves a reproducible experimental suite benchmarking the proposed approach against standard baselines across varying loads and system configurations, supported by statistically rigorous comparative analysis. Full project Desciption and Requiremnts : ﻿Optimization-Based Multi-Objective Task Offloading in IoT–Fog–Cloud Systems: An Energy–Delay Trade-off Framework&nbsp; &nbsp;Objective 1 — Formulate the optimization problem&nbsp;Description (short): Build a constrained multi-objective model for offloading decisions that minimizes energy and delay under deadline and resource limits.&nbsp;Expected outputs:&nbsp;<ul><li>Mathematical formulation (variables, objectives, constraints).&nbsp;</li><li>Feasibility handling rules (deadline/memory/capacity, one-choice-per-task).&nbsp;</li><li>Defined evaluation metrics and benchmark scenario specifications.&nbsp;</li></ul> &nbsp;Objective 2 — Develop the optimization solver&nbsp;Description (short): Implement a baseline multi-objective optimizer (e.g., NSGA-II) and a hybrid enhancement (e.g., DE-based operator or local refinement) to improve search quality and stability.&nbsp;Expected outputs:&nbsp;<ul><li>Implemented solver(s): baseline + improved variant, with documented settings.&nbsp;</li><li>Ablation results showing the contribution of the enhancement module.&nbsp;</li><li>Pareto-front generation and a practical selection rule for operating points.&nbsp;</li></ul> &nbsp;Objective 3 — Validate performance and robustness&nbsp;Description (short): Benchmark the proposed solver against standard baselines across different loads and system settings with statistically supported analysis.&nbsp;Expected outputs:&nbsp;<ul><li>Reproducible experimental suite (scenarios, seeds, parameter settings).&nbsp;</li><li>Result artifacts: Pareto fronts, energy–delay curves, convergence/runtime, feasibility rate.&nbsp;</li><li>Comparative analysis and final conclusions (including limitations and reproducibility notes).&nbsp;</li></ul>&nbsp;References: <a href="https://link.springer.com/article/10.1007/s12652-021-03388-2" rel="noopener noreferrer" target="_blank" style="color: rgb(70, 120, 134); background-color: transparent;">https://link.springer.com/article/10.1007/s12652-021-03388-2</a>

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