Thesis Topic Ideas: Federated Learning for Privacy-Preserving Healthcare Data in Nepal

1. Introduction & Problem Statement

Overview: Architecting a decentralized, privacy-preserving machine learning framework allowing hospitals in Nepal to train shared diagnostic AI models without centralizing raw patient records.

Background Context (Nepal): Medical data privacy regulations prevent centralizing sensitive patient health records across different hospital databases. Federated Learning trains local models collaboratively while keeping raw patient data on-site.

2. Research Objectives

• ›Design a Federated Learning architecture (FedAvg / FedProx) for multi-hospital diagnostic classification
• ›Simulate decentralized hospital nodes using privacy-preserving differential privacy techniques
• ›Train shared diagnostic vision model (X-ray pneumonia classification) across partitioned hospital data
• ›Evaluate global model convergence, communication overhead, and resilience against privacy attacks
• ›Benchmark diagnostic performance against centralized training baselines

3. Proposed Methodology

1. Partition public medical image dataset (Chest X-Ray) across 5 simulated hospital client nodes
2. Implement Federated Learning pipeline using PyTorch and Flower (Flwr) framework
3. Apply Differential Privacy noise injection to model parameter weight updates during aggregation rounds
4. Measure global validation accuracy, loss convergence, and network payload size per round
5. Conduct security analysis evaluating gradient leakage risks

$ Worked Example / Sample Scenario

Sample Scenario: Training a shared Chest X-ray diagnostic model across 5 simulated hospital nodes in Flower (Flwr) achieves 91.4% accuracy while Differential Privacy (ε = 2.5) guarantees zero raw patient image transfer.

4. Thesis Chapter-by-Chapter Outline

5. Recommended Tools & Technologies

To implement the practical, technical, or analytical portions of this thesis topic, the following software tools, libraries, or APIs are recommended:

6. Core References & Academic Sources

• [1]IEEE Transactions on Medical Imaging <!-- VERIFY: needs confirmation -->
• [2]ACM Computing Surveys — Federated Learning in Healthcare <!-- VERIFY: needs confirmation -->

7. Frequently Asked Questions (FAQs)

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