Papers

1. Blockchain-Enhanced Client Selection and AdaTrim for Secure Federated Learning in IoT Networks
Status: Submitted

2. Trimmed Averaging for Efficient Federated Learning in the Internet of Things
Status: Submitted

3. Impact of neural network Configurations in Federated Learning Optimization for Intrusion Detection in Edge-Iot devices (Tentative)

Last Week's Progress

3. Impact of neural network Configurations in Federated Learning Optimization for Intrusion Detection in Edge-Iot devices (Tentative)
-Skimming a draft for the next paper to write relative to the future directions in the submitted papers

This Week Todo's

3. Impact of neural network Configurations in Federated Learning Optimization for Intrusion Detection in Edge-Iot devices (Tentative)
- Downloading papers for reference so as to do a comprehensive literature review
- Writing the introduction to the paper

Project Progress

1. Digital Twin (18%)
2. Purepet (80 %)

Last Week's Project

1. Digital Twin (18%)
- Studying Python web framework(Flask and Django)
2. Purepet (80 %)
-Intergrating the mo0bile application with the web application

This Week's Project

1. Digital Twin (18%)
- Modifying the web interface for the Web application of the digital twin
2. Purepet (80 %)
-Further modifying the app interface design

Monthly Goals

Continue working toward optimizing the Federated Average algorithm to improve its robustness and efficiency. This includes incorporating insights from the literature review on Edge IoT, applying QAT to the neural network model, and fine-tuning the model with new datasets.

Annual Goals

1. Submit at least two articles to scientific journals.
2. Submit and attend at least one NSL-approved domestic conference.
3. Submit and attend at least one NSL-approved international conference.