Privacy Preserving Recommender Systems using federated learning
Budget: £20 – £250 GBP
I am looking for an experienced AI/ML expert to assist with a project focused on a privacy-preserving recommender system that utilizes federated learning (Code will be provided). The project requires the following tasks:
1. Code Execution:
- Run the provided code for a federated learning-based recommender system.
- Document each step of the execution process, including configurations, dependencies, and environment setup.
- Take clear, high-quality screenshots of each significant step, including the setup, training process, and results.
2. Code Explanation:
- Provide a detailed explanation of the code, including the purpose of each module, the flow of the program, and how federated learning is implemented in the system.
- Clarify any complex or critical sections of the code to ensure a comprehensive understanding.
3. Literature Review:
- Write a well-researched literature review related to privacy-preserving recommender systems and federated learning.
- Use Harvard-style citations and referencing for all sources.
- The literature review should cover key topics, such as:
- Overview of recommender systems and their applications.
- Privacy concerns in traditional recommender systems.
- The role of federated learning in enhancing privacy.
- Existing work and research gaps in privacy-preserving recommender systems.
Requirements:
- Strong understanding of machine learning, especially federated learning.
- Experience in running and debugging Python code.
- Ability to explain complex technical concepts in a clear and concise manner.
- Proficiency in academic writing, particularly in composing literature reviews with Harvard-style citations.
Deliverables:
- A fully executed and documented code with screenshots for each step.
- A comprehensive explanation of the code.
- A 2000-3000 word literature review with Harvard-style citations and referencing.
If you have the expertise to help with this project, I would love to work with you! Please provide examples of similar work you have done in the past.
1. Code Execution:
- Run the provided code for a federated learning-based recommender system.
- Document each step of the execution process, including configurations, dependencies, and environment setup.
- Take clear, high-quality screenshots of each significant step, including the setup, training process, and results.
2. Code Explanation:
- Provide a detailed explanation of the code, including the purpose of each module, the flow of the program, and how federated learning is implemented in the system.
- Clarify any complex or critical sections of the code to ensure a comprehensive understanding.
3. Literature Review:
- Write a well-researched literature review related to privacy-preserving recommender systems and federated learning.
- Use Harvard-style citations and referencing for all sources.
- The literature review should cover key topics, such as:
- Overview of recommender systems and their applications.
- Privacy concerns in traditional recommender systems.
- The role of federated learning in enhancing privacy.
- Existing work and research gaps in privacy-preserving recommender systems.
Requirements:
- Strong understanding of machine learning, especially federated learning.
- Experience in running and debugging Python code.
- Ability to explain complex technical concepts in a clear and concise manner.
- Proficiency in academic writing, particularly in composing literature reviews with Harvard-style citations.
Deliverables:
- A fully executed and documented code with screenshots for each step.
- A comprehensive explanation of the code.
- A 2000-3000 word literature review with Harvard-style citations and referencing.
If you have the expertise to help with this project, I would love to work with you! Please provide examples of similar work you have done in the past.
Related categories:
Machine Learning (ML)
Deep Learning
Natural Language Processing
Recommendation System