Degree
Master of Science in Data Science
Department
Department of Computer Science
Faculty/ School
School of Mathematics and Computer Science (SMCS)
Date of Submission
Fall 2024
Supervisor
Ms. Tasbiha Fatima, Lecturer, Department of Computer Science
Keywords
Federated Learning, Differential Privacy, MNIST, Deep Learning, Gradient Clipping
Abstract
This project explores the application of federated learning combined with differential privacy (DP) for training a deep learning model on the MNIST dataset. Federated learning enables distributed training across multiple clients while keeping data decentralized, which is essential for privacy preservation. The introduction of differential privacy further ensures that individual client data remains protected by adding noise to the model's gradients during training, thereby limiting the exposure of sensitive information.
The code implemented in this project focuses on setting up a federated learning framework and employing a deep neural network for classification of digits from the MNIST dataset. Parameters of differential privacy are added including noise multipliers and gradient clipping values to ensure preservation of user data.
The dataset is divided into three partitions, each excluding certain digit groups to simulate non-IID (non-independent and identically distributed) data scenarios. Federated averaging strategy is applied for global model updates.
Evaluation metrics, including model accuracy and confusion matrices, are computed after each round of training. The results indicate that the proposed approach can maintain high model performance while respecting differential privacy constraints.
Overall, this project demonstrates how federated learning with differential privacy can provide a practical solution for training machine learning models securely across distributed datasets, paving the way for privacy-preserving applications in machine learning.
Document Type
Restricted Access
Submission Type
Research Project
Recommended Citation
Qureshi, M. O. (2024). Federated Learning for Privacy-Preservation through Differential Privacy Protocol (Unpublished graduate research project). Institute of Business Administration, Pakistan. Retrieved from https://ir.iba.edu.pk/research-projects-msds/64
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