WRITE ME MAKE A PN(Project Network) 2.3. Project Network (PN)The PN illustrates the connection between tasks;that is,which tasks precede others,and which tasks require interaction with other tasks.The PN can inherit elements from the WBS,but additionally provides information about the sequence and interactions of tasks.A simple example PN is given in the report template.Adapt that example to your project and add more tasks and interactions.Add a discussion in the text to clarify any important details.2.6. Risk analysis Consider significantrisks to the success of your project, list and evaluate them, determine and state your plans for mitigating these risks. Do this for the whole project (all sub-systems). Two simple example tables (Risk Matrix and Risk assessment table) are provided in the report template; adapt them for your project.* Claims for reimbursement of costs will be compared with this table -make sure that it is accurate.

Project Name:Privacy-Preserving Face Recognition Using Federated Machine Learning THINGS YOU NEED TO DO: I was given the task of researching the Federated Machine Learning implementation and how the client-server relationships of federated applications work in real life applications, collecting sample repos, etc. Project Purpose:This project aims to develop an application that uses face recognition technology while ensuring the privacy of participant by using 'Flower' federated machine learning library and the PyThorch framework.The project can be applied in various scenarios that require individuals to be recognized using face recognition technology, such as secure for entry face applications or face recognition applications on personal devices Project Description:The system will be designed to process data on local devices without centralizing it on application servers.The large scale celeb-faces attributes (CelebA) dataset will be used for model training. Project Requirements:Outputs:Federated face recognition model A face recognition model will be developed using Flower federated machine learning and Keras/TensorFlow.The model will process user data locally,ensure a secure and private enviroment.Data Privacyand Test Report:At,the projet's conclusion, a report will be presented, detailing data privacy applications and the use of Flower with Keras/TensorFlow .This report will provide an in depth explanation of the project's privacy-oriented approach and applications.Draft Work Packages Project Planning and Design:Defining project objective and requirements.Designing and integration of flower plannig the use of large scale celeb face attributes(CelebA) Project Constraints: Technology stack:ML/AI Keras/TensorFlow and scikit-learn FederatedML Framework:Flower Software Dev:Phyton,Java,JavaSc Agile Scrum LifeCycle Tool:Atlassian jira standrarts:IEEE & PMI

\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|} \hlineTasksNames & \begin{tabular}{l} Goksu \\ Ceylan \end{tabular} & \begin{tabular}{l} Alper \\ Cakir \end{tabular} & \begin{tabular}{c} tclal \\ Snmez \end{tabular} & \begin{tabular}{c} Eren Ard \\ Celebi \end{tabular} & \begin{tabular}{l} Okan \\ mar \end{tabular} & \begin{tabular}{l} Gungor \\ Parlak \end{tabular} & & \begin{tabular}{c} Ozan \\ Kurkut \end{tabular} & \begin{tabular}{c} Ulas \\ Rotitida \\ Gnler \end{tabular} \\ \hline Researching & R & S & S & R & S & S & S & S & R \\ \hline Planning & & R & S & R & S & & & S & S \\ \hline \begin{tabular}{c} Antificial \\ Intelligence \end{tabular} & R & R & R & S & S & S & S & S & S \\ \hline Communication & S & S & & & S & & R & R & R \\ \hline Control & S & & S & R & R & R & S & & S \\ \hline Integration & S & S & R & S & R & S & R & S & S \\ \hline Reporting & S & S & R & S & S & R & S & R & S \\ \hline \end{tabular} Figure 5. The project network